Cause-related marketing has risen to become a popular strategy to increase business value through profit-motivated giving. Despite the growing number of articles published in the last decade, no comprehensive analysis of the most discussed constructs of cause-related marketing is available. This paper uses an advanced Text Mining methodology to conduct a comprehensive analysis of 246 articles published in 40 different journals between 1988 and 2013 on the subject of cause-related marketing. Text Mining also allows quantitative analyses (...) to be performed on the literature. For instance, it is shown that the most prominent long-term topics discussed since 1988 on the subject are “brand-cause fit”, “law and Ethics”, and “corporate and social identification”, while the most actively discussed topic presently is “sectors raising social taboos and moral debates”. The paper has two goals: first, it introduces the technique of CTM to the Marketing area, illustrating how Text Mining may guide, simplify, and enhance review processes while providing objective building blocks to be used in a review; second, it applies CTM to the C-RM field, uncovering and summarizing the most discussed topics. Mining text, however, is not aimed at replacing all subjective decisions that must be taken as part of literature review methodologies. (shrink)

Due to existence of a huge amount of textual data either on the World Wide Web or in textual databases like PubMed, the development of novel automatic keyphrase extraction methods has emerged as one of the key research problems in recent past. Consequently, a number of machine learning techniques, mostly supervised, have been proposed to extract keyphrases from text documents. But, one of the main bottlenecks that hinders the success of such systems is the requirement of annotated corpora for (...) training purpose. In this paper, we propose the design of a deep text mining system to identify keyphrases in text documents that are either unstructured or semi-structured in nature. The novelty of our system lies in its applicability on a single document, instead of demanding a collection of annotated texts for training, to identify keyphrases embedded within it. The proposed system applies parsing techniques to identify candidate phrases. After mapping the original set of candidate phrases into a low-dimensional space using Singular Value Decomposition, the Markov Clustering technique is applied to cluster related sentences together. Finally, considering each cluster as a document, Latent Dirichlet Allocation is applied to identify feasible keyphrases that are presented to users in non-increasing order of their relevance score values. The efficacy of the proposed system is established through experimentation on datasets from two different domains. On comparative evaluation, we found that the proposed system outperforms KEA and KEA that apply the supervised machine learning approach for automatic keyphrase extraction from text documents. (shrink)

Financial risks are among the most important risks in the construction industry projects, which significantly impact project objectives, including project cost. Besides, financial risks have many interactions with each other and project parameters, which must be taken into account to analyze risks correctly. In addition, a source of financial risks in a project is the contract, which is the most important project document. Identifying terms related to financial risks in a contract and considering their effects on the risk management process (...) is an essential issue that has been neglected. Hence, an integrated model for evaluating financial risks and their related contractual clauses were presented. To this end, the effect of financial risks on the project cost was simulated using a system dynamics model. Moreover, terms related to financial risks in a contract text were identified and extracted using text mining, and their effect was included in the system dynamics model. The model was implemented in a hospital construction project in Tehran as a case study, and its results were analyzed. The innovation of the research is integrating text mining and the system dynamics model to investigate the effect of financial risks and related contractual clauses on the project cost. (shrink)

This article introduces a new hybrid intake procedure developed for posttraumatic stress disorder (PTSD) screening, which combines an automated textual assessment of respondents’ self-narratives and item-based measures that are administered consequently. Text mining technique and item response modeling were used to analyze long constructed response (i.e., self-narratives) and responses to standardized questionnaires (i.e., multiple choices), respectively. The whole procedure is combined in a Bayesian framework where the textual assessment functions as prior information for the estimation of the PTSD (...) latent trait. The purpose of this study is twofold: first, to investigate whether the combination model of textual analysis and item-based scaling could enhance the classification accuracy of PTSD, and second, to examine whether the standard error of estimates could be reduced through the use of the narrative as a sort of routing test. With the sample at hand, the combination model resulted in a reduction in the misclassification rate, as well as a decrease of standard error of latent trait estimation. These findings highlight the benefits of combining textual assessment and item-based measures in a psychiatric screening process. We conclude that the hybrid test design is a promising approach to increase test efficiency and is expected to be applicable in a broader scope of educational and psychological measurement in the future. (shrink)

Techniques of text data analysis have been known for many years and commonly used in many areas of life. Text mining enables, among others, the acquisition of information from the text, its filtering, and studying of similarities and relationships. The aim of this paper is to design a method that would make it possible to assess the health quality of dietary supplements, on the basis of text mining techniques. A fictional plant-based product was used (...) in the study, which was compared with other products containing at least one of the tested ingredients registered in the years 2007–2019 in the register of dietary supplements kept by the Chief Sanitary Inspectorate (GIS), which were given either the “consistent” or “to be clarified” status. The obtained results concern the frequency of occurrence of the individual ingredients (St John’s wort/Hypericum, melissa, rose root/Rhodiola) in other products, considering their status in the register. The data thus obtained was subjected to classical statistical analysis in order to find correlations between the presence of a given ingredient and the product status. In view of the obtained results, the text mining analysis may be considered as a helpful tool in the process of internal risk assessment performed by manufacturers of dietary supplements. (shrink)

\noindent\textbf{Introduction} Online interventions hold great potential for Therapeutic Change Process Research (TCPR), a field that aims to relate in-therapeutic change processes to the outcomes of interventions. Online a client is treated essentially through the language their counsellor uses, therefore the verbal interaction contains many important ingredients that bring about change. TCPR faces two challenges: how to derive meaningful change processes from texts, and secondly, how to assess these complex, varied and multi-layered processes? We advocate the use text mining (...) and multi-level models (MLMs): the former offers tools and methods to discovers patterns in texts; the latter can analyse these change processes as outcomes that vary at multiple levels. \noindent\textbf{Method} We (re-)used the data from \cite{Lamers2015} because it includes outcomes and the complete online intervention for clients with mild depressive symptoms. We used text mining to obtain basic text-variables from e-mails, that we analysed through MLMs. \noindent\textbf{Results} We found that we could relate outcomes of interventions to variables containing text-information. We conclude that we can indeed bridge text mining and MLMs for TCPR as it was possible to relate text-information (obtained through text mining) to multi-levelled TCPR outcomes (using a MLM). \noindent\textbf{Discussion} Text mining can be helpful to obtain change processes, which is also the main challenge for TCPR. We showed how MLMs and text mining can be combined, but our proposition leaves open how to obtain the most relevant textual operationalization of TCPR concepts. That requires interdisciplinary collaboration and discussion. The future does look bright: based on our proof-of-concept study we conclude that MLMs and text mining can indeed advance TCPR. (shrink)

A recent systematic review of Machine Learning (ML) approaches to health data, containing over 100 studies, found that the most investigated problem was mental health (Yin et al., 2019). Relatedly, recent estimates suggest that between 165,000 and 325,000 health and wellness apps are now commercially available, with over 10,000 of those designed specifically for mental health (Carlo et al., 2019). In light of these trends, the present chapter has three aims: (1) provide an informative overview of some of the recent (...) work taking place at the intersection of text mining and mental health so that we can (2) highlight and analyze several pressing ethical issues that are arising in this rapidly growing field and (3) suggest productive directions for how these issues might be better addressed within future interdisciplinary work to ensure the responsible development of text mining approaches in psychology generally, and in mental health fields, specifically. In Section 1, we review some of the recent literature on text-mining and mental health in the contexts of traditional experimental settings, social media, and research involving electronic health records. Then, in Section 2, we introduce and discuss ethical concerns that arise before, during, and after research is conducted. Finally, in Section 3, we offer several suggestions about how ethical oversight of text-mining research might be improved to be more responsive to the concerns mapped out in Section 2. (shrink)

Public’s concern about poor animal welfare provided by intensive farming systems has increased over the last decades. This study reviewed the interest of the scientific research on the pain issue in pig production to assess if the societal instances may be a driving force for the research activity. A literature search protocol was set up to identify the peer-reviewed papers published between 1970 and 2017 that covered the topic of ‘pain in pigs’ using Scopus®, database of Elsevier©. One hundred and (...) thirty papers were selected and they were mainly focused on the practice of castration followed by tail docking. The scientific community first focused on these painful practices as a way to improve production efficiency and quality issues while more recently, due to the increased pressure by the public opinion, turned its interest towards the search of alternative solutions. A text mining analysis on the abstract of the selected papers clearly indicated the effort of the research to explore solutions to alleviate pain. Evocative words of this target were the selected terms ‘pharmacological analgesic’ and ‘anaesthetic treatments’. The text mining highlighted vocalizations as the main pain indicators in pigs as this term was frequently associated to ‘acute stress’. Ethical issues were a minor research topic in the scientific literature on pig breeding but in the short run, they are supposed to become a major subject to justify the acceptance of the modern production systems at the eyes of the consumers. (shrink)

With the emergence of online communities, more and more people are participating in online technology communities to meet personalized learning needs. This study aims to investigate whether and how male and female users behave differently in online technology communities. Using text data from Python Technology Community, through LDA (Latent Dirichlet Allocation) model, sentiment analysis and regression analysis, this paper reveals the different topics of male and female users in the online technology community, their sentimental tendencies and activity under different (...) topics, and their correlation and their mutual influence. The results show that: (1) Male users tend to provide information help, while female users prefer to participate in the topic of making friends and advertising. (2) When communicating in the technology community, male and female users mostly express positive emotions, but female users express positive emotions more frequently. (3) Different emotional tendencies of male and female users under different topics have different effects on their activity in the community. The activity of female users is more susceptible to emotional orientation. (shrink)

Argumentation mining aims to automatically detect, classify and structure argumentation in text. Therefore, argumentation mining is an important part of a complete argumentation analyisis, i.e. understanding the content of serial arguments, their linguistic structure, the relationship between the preceding and following arguments, recognizing the underlying conceptual beliefs, and understanding within the comprehensive coherence of the specific topic. We present different methods to aid argumentation mining, starting with plain argumentation detection and moving forward to a more structural (...) analysis of the detected argumentation. Different state-of-the-art techniques on machine learning and context free grammars are applied to solve the challenges of argumentation mining. We also highlight fundamental questions found during our research and analyse different issues for future research on argumentation mining. (shrink)

Argumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major conferences and journals of the field. Its goals are to identify argumentation in text or dialogue; to construct representations of the constellation of claims, supporting and attacking moves (in different levels of detail); and to characterize the patterns of (...) reasoning that appear to license the argumentation. Furthermore, recent work also addresses the difficult tasks of evaluating the persuasiveness and quality of arguments. Some of the linguistic genres that are being studied include legal text, student essays, political discourse and debate, newspaper editorials, scientific writing, and others. The book starts with a discussion of the linguistic perspective, characteristics of argumentative language, and their relationship to certain other notions such as subjectivity. Besides the connection to linguistics, argumentation has for a long time been a topic in Artificial Intelligence, where the focus is on devising adequate representations and reasoning formalisms that capture the properties of argumentative exchange. It is generally very difficult to connect the two realms of reasoning and text analysis, but we are convinced that it should be attempted in the long term, and therefore we also touch upon some fundamentals of reasoning approaches. Then the book turns to its focus, the computational side of mining argumentation in text. We first introduce a number of annotated corpora that have been used in the research. From the NLP perspective, argumentation mining shares subtasks with research fields such as subjectivity and sentiment analysis, semantic relation extraction, and discourse parsing. Therefore, many technical approaches are being borrowed from those (and other) fields. We break argumentation mining into a series of subtasks, starting with the preparatory steps of classifying text as argumentative (or not) and segmenting it into elementary units. Then, central steps are the automatic identification of claims, and finding statements that support or oppose the claim. For certain applications, it is also of interest to compute a full structure of an argumentative constellation of statements. Next, we discuss a few steps that try to 'dig deeper': to infer the underlying reasoning pattern for a textual argument, to reconstruct unstated premises (so-called 'enthymemes'), and to evaluate the quality of the argumentation. We also take a brief look at 'the other side' of mining, i.e., the generation or synthesis of argumentative text. The book finishes with a summary of the argumentation mining tasks, a sketch of potential applications, and a—necessarily subjective—outlook for the field. Table of Contents: Preface / Acknowledgments / Introduction / Argumentative Language / Modeling Arguments / Corpus Annotation / Finding Claims / Finding Supporting and Objecting Statements / Deriving the Structure of Argumentation / Assessing Argumentation / Generating Argumentative Text / Summary and Perspectives / Bibliography / Authors' Biographies / Index. (shrink)

In this paper we look at the manual analysis of arguments and how this compares to the current state of automatic argument analysis. These considerations are used to develop a new approach combining a machine learning algorithm to extract propositions from text, with a topic model to determine argument structure. The results of this method are compared to a manual analysis.

The dominant approach to argument mining has been to treat it as a machine learning problem based upon superficial text features, and to treat the relationships between arguments as either support...

Discovering high-utility itemsets (HUIs) consists of finding sets of items that yield a high profit in customer transaction databases. An important limitation of traditional high-utility itemset mining (HUIM) is that only the utility measure is used for assessing the interestingness of patterns. This leads to finding several itemsets that have a high profit but contain items that are weakly correlated. To address this issue, this paper proposes to integrate the concept of correlation in HUIM to find profitable itemsets that (...) are highly correlated, using the all-confidence and bond measures. An efficient algorithm named FCHM (fast correlated high-utility itemset miner) is proposed to efficiently discover correlated high-utility itemsets (CHIs). Two versions of the algorithm are proposed: FCHM|$_{all\text{-}confidence}$| and FCHM|$_{bond}$|⁠, which are based on the all-confidence and bond measures, respectively. An experimental evaluation was done using four real-life benchmark datasets from the HUIM literature: mushroom, retail, kosarak and foodmart. Results show that FCHM is efficient and can prune a huge amount of weakly CHIs. (shrink)

Argumentation mining is an advanced form of human language understanding by the machine. This is a challenging task for a machine. When sufficient explicit discourse markers are present in the lang...

One of the crucial fields of study that is getting more attention is association rule mining. It is crucial to databases' knowledge discovery (KDD). KDD and association rule mining have a very broad use. It has evolved at a rapid rate over the past fifteen years. Association Rule Mining is a novel technology, although it is still in the discovery and development stages. Video is an illustration of interactive media data since it contains text, pictures, meta-data, (...) visual, sound, and different kinds of data. It is frequently used for a variety of important likely reasons, such as security and reconnaissance, amusement, healthcare, educational endeavours, and sports. One of the main issues for the local data-mining research community is video data mining, which aims to find and comprehend fascinating examples from the large volume of video data. Video data mining is still in its infancy when compared to mining other sorts of data. It is pivotal to grasp and naturally mine the information from video databases considering the continually extending computerized libraries and video databases. Differentiating association rules between components in a significant video library is critical in the field of video data mining. As per continuous imaginative work, association rule mining is being utilized rapidly in various tasks, including perception, social affairs, broadcast news, sports, records, films, clinical data, as well as on the web and individual media assortments. This exploration tries to give a far-reaching method to association rule extraction from video databases. (shrink)

Traditional document clustering algorithms consider text-based features such as unique word count, concept count, etc. to cluster documents. Meanwhile, event mining is the extraction of specific events, their related sub-events, and the associated semantic relations from documents. This work discusses an approach to event mining through clustering. The Universal Networking Language -based subgraph, a semantic representation of the document, is used as the input for clustering. Our research focuses on exploring the use of three different feature sets (...) for event clustering and comparing the approaches used for specific event mining. In our previous work, the clustering algorithm used UNL-based event semantics to represent event context for clustering. However, this approach resulted in different events with similar semantics being clustered together. Hence, instead of considering only UNL event semantics, we considered assigning additional weights to similarity between event contexts with event-related attributes such as time, place, and persons. Although we get specific events in a single cluster, sub-events related to the specific events are not necessarily in a single cluster. Therefore, to improve our cluster efficiency, connective terms between two sentences and their representation as UNL subgraphs were also considered for similarity determination. By combining UNL semantics, event-specific arguments similarity, and connective term concepts between sentences, we were able to obtain clusters for specific events and their sub-events. We have used 112 000 Tamil documents from the Forum for Information Retrieval Evaluation data corpus and achieved good results. We have also compared our approach with the previous state-of-the-art approach for Router-RCV1 corpus and achieved 30% improvements in precision. (shrink)

This contribution uses the career and writings of Juan Solórzano Pereira (1575–1655) to probe the relationship between mercury, governance, and the obligations of individuals to the early modern Iberian state. It focuses specifically on two terms often employed in the context of practical governance—“bien público” (public good) and “hacienda” (treasury)—by placing Solórzano Pereira’s 1647 Politica Indiana and administrative documents generated during his tenure at the mercury mine of Huancavelica (modern Peru) in dialogue. Read in tandem, these texts reveal that Solórzano (...) Pereira articulated the relationship between mercury, the bien público, and the hacienda through comparisons between mining and agriculture, a conception of nature as an agential force, and period understandings of the “common.” While historians of practical governance often dismiss appeals to the bien público and the hacienda as hollow rhetoric, this essay reveals that the terms were important for period actors in conceptualizing the distribution of labor and materials. (shrink)

Interaction mining is about discovering and extracting insightful information from digital conversations, namely those human?human information exchanges mediated by digital network technology. We present in this article a computational model of natural arguments and its implementation for the automatic argumentative analysis of digital conversations, which allows us to produce relevant information to build interaction business analytics applications overcoming the limitations of standard text mining and information retrieval technology. Applications include advanced visualisations and abstractive summaries.

Des recherches récentes dans la chôra thasienne ont apporté une éclatante confirmation du texte d'Hérodote (VI, 46-47) évoquant les ressources minières de Thasos. Sur la côte orientale, entre Potamia et Kinyra, ont été repérées de nombreuses mines réparties en trois secteurs sur le versant de la montagne. L'or est le seul métal contenu en quantité suffisante pour être exploitable dans les prélèvements de minerai. Les autres mines évoquées allusivement par Hérodote sont des mines d'or (Limenas), ou de plomb argentifère, de (...) cuivre et de fer (côté occidental de l'île). (shrink)

From a purely formalist viewpoint on the philosophy of mathematics, experiments cannot (and should not) play a role in warranting mathematical statements but must be confined to heuristics. Yet, due to the incorporation of new mathematical methods such as computer-assisted experimentation in mathematical practice, experiments are now conducted and used in a much broader range of epistemic practices such as concept formation, validation, and communication. In this article, we combine corpus studies and qualitative analyses to assess and categorize the epistemic (...) roles experiments are seen—by mathematicians—to have in actual mathematical practice. We do so by text-mining a corpus of reviews from the _Mathematical Reviews_, which include the indicator word “experiment”. Our qualitative, grounded classification of samples from this corpus allows us to explore the various roles played by experiments. We thus identify instances where experiments function as references to established knowledge, as tools for heuristics or exploration, as epistemic warrants, as communication or pedagogy, and instances simply proposing experiments. Focusing on the role of experiments as epistemic warrants, we show through additional sampling that in some fields of mathematics, experiments can warrant theorems as well as methods. We also show that the expressed lack of experiments by reviewers suggests concordant views that experiments could have provided epistemic warrants. Thus, our combination of corpus studies and qualitative analyses has added a typology of roles of experiments in mathematical practice and shown that experiments can and do play roles as epistemic warrants depending on the mathematical field. (shrink)

One of the main challenges for computational legal research is drawing up innovative heuristics to derive actionable knowledge from legal documents. While a large part of the research has been so far devoted to the extraction of purely legal information, less attention has been paid to seeking out in the texts the clues of more complex entities: legally relevant facts whose detection requires to link and interpret, as a unified whole, legal information and results of empirical analyses. This paper presents (...) an ongoing research that points in this direction, trying to devise new ways to support public prosecutors in assessing the dangerousness of individuals and groups under investigation, an activity that precisely relies on the cross-sectional evaluation of legal and empirical data. A knowledge mining strategy will be outlined that lines up, into a single metaheuristic model, information extraction, network-based inference, machine learning and visual analytics. We will focus, in particular, on the integration of graph-based inference and machine learning methods used both to support classification tasks and to explore new forms of man-machine cooperation. Experiments made involving public prosecutors from the Italian Anti-Mafia Investigation Directorate and using data from real investigations have not only shown the potentialities of our approach but also offered an opportunity to reflect on the role we could assign to AI when thinking about the future of legal science and practice. (shrink)

The very expertise with which psychologists wield their tools for achieving laboratory control may have had the unwelcome effect of blinding psychologists to the possibilities of discovering principles of behavior without conducting experiments. When creatively interrogated, a diverse range of large, real-world data sets provides powerful diagnostic tools for revealing principles of human judgment, perception, categorization, decision-making, language use, inference, problem solving, and representation. Examples of these data sets include patterns of website links, dictionaries, logs of group interactions, collections of (...) images and image tags, text corpora, history of financial transactions, trends in twitter tag usage and propagation, patents, consumer product sales, performance in high-stakes sporting events, dialect maps, and scientific citations. The goal of this issue is to present some exemplary case studies of mining naturally existing data sets to reveal important principles and phenomena in cognitive science, and to discuss some of the underlying issues involved with conducting traditional experiments, analyses of naturally occurring data, computational modeling, and the synthesis of all three methods. (shrink)

Available corpora for Argument Mining differ along several axes, and one of the key differences is the presence (or absence) of discourse markers to signal argumentative content. Exploring effective ways to use discourse markers has received wide attention in various discourse parsing tasks, from which it is well-known that discourse markers are strong indicators of discourse relations. To improve the robustness of Argument Mining systems across different genres, we propose to automatically augment a given text with discourse (...) markers such that all relations are explicitly signaled. Our analysis unveils that popular language models taken out-of-the-box fail on this task; however, when fine-tuned on a new heterogeneous dataset that we construct (including synthetic and real examples), they perform considerably better. We demonstrate the impact of our approach on an Argument Mining downstream task, evaluated on different corpora, showing that language models can be trained to automatically fill in discourse markers across different corpora, improving the performance of a downstream model in some, but not all, cases. Our proposed approach can further be employed as an assistant tool for better discourse understanding. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:All Mine!: Happiness, Ownership, and Naming in Eleventh-Cenury China by Stephen OwenNguyen T. Thanh-Huyen (bio)All Mine!: Happiness, Ownership, and Naming in Eleventh-Cenury China. By Stephen Owen. New York: Columbia University Press, 2021. Pp. 208. Paperback $30.00, isbn 978-0-231-20311-1. Reading Stephen Owen's new book, All Mine!: Happiness, Ownership, and Naming in Eleventh-Century China (hereafter All Mine!), many readers will find that the perspectives of eleventh-century Song scholar-officials on finding (...) happiness in material things are still highly relevant to the increasingly materialistic culture in China and the world in the present day. Under the Song dynasty, the pursuit of happiness through material objects is "what contemporaries themselves recognized as uncomfortable topics" (p. 14). In writing this book, Owen attempts to survey a literary phenomenon in which material-contingent happiness permeated Song literature despite the moral surveillance of classical ideology on happiness and ownership that stretched back to the Lunyu (Analects of Confucius). At the same time, the author elucidates how material-contingent happiness in literature drove and was driven by the sociocultural changes and the growth of commercialization in the Song. All Mine! is a fine balance between literary study and intellectual history. Owen's approach tends to be all-inclusive, as he covers a diverse range of genres (including prose, poetry, miscellany, and biography) and texts with which readers generally have different levels of familiarity. For every text, Owen provides thorough explanation on its background, allusions, and references, while also offering in-depth cultural, aesthetic, and structural analysis. He calls our attention to the subtle differences between similar texts, such as the biographies of Ouyang Xiu 歐陽脩 and Tao Yuanming 陶淵明, to show how they deliver entirely different ideas about happiness and ownership and signal a radical transition from the old to a new world of values. Regarding intellectual history, the book studies the conflicts between different discourses on happiness. Owen reveals different forces of censorship against happiness of acquisition and ownership, presenting them especially clearly with Ouyang Xiu's stories in Chapter 1 ("The Biography of the Retired Layman Six") and Chapter 2 ("The Magistrate of Peach Blossom Spring"). The author also pays close attention to the act of naming as a form of ownership. As he points out, the process of Song literati trying to "get the right name" for objects and sites often converged with the process of elaborating different [End Page 1] person-thing relationships based on the social status of the owner and the characteristics of the object of ownership, thereby grounding the principles for judging the morality of material-contingent happiness. Readers can also see the happiness-ownership paradox in literature as closely related to the commercialization of elite-praised goods that was flourishing in Song society. This validates Owen's choice "to think of cultural and historical phenomena as ecosystems or solar systems in constant change, each particular changed by and changing the whole" (p. 13). Perhaps the most impressive chapters in the book are 3 and 5, which deal with the Song's perspective on ownership as transient and unstable, and its relationship with happiness. Chapter 3 explores the pervasive awareness that "things are held by power, not by right" (p. 67). The source of "power" could be financial, social, political, and military advantage, or something humans can't control, like death. Owen relates many sad Song stories of elites whose things were taken by someone with greater power. The most interesting one is perhaps that of Su Dongpo 苏東坡 and his "Nine Blossoms Mountain in a Jug," in which Su was not willing to give up the little world that he attached to this rock mountain, despite being very conscious about the dangers of possession. "It is alright if the superior man lets his interests be temporarily invested in things, but it is not alright if he lets his interest remain in those things," the author writes (p. 72). Chapter 3 contains many examples of "names as a form of ownership." Ownership of physical things can be transient and infringeable, but the writings on names of objects and sites by famous Song literati like Ouyang Xiu and Su Dongpo... (shrink)

Summary From summer 1792 until spring 1797, Alexander von Humboldt was a mining official in the Franconian parts of Prussia. He visited mines, inspected smelting works, calculated budgets, wrote official reports, founded a mining school, performed technological experiments, and invented a miners’ lamp and respirator. At the same time he also participated in the Republic of Letters, corresponded with savants in all Europe, and was a member of the Leopoldine Carolinian Academy and the Berlin Gesellschaft Naturforschender Freunde. He (...) collected minerals, made geognostic observations, performed chemical and physiological experiments, read the newest scientific journals, and prepared and published texts on mineralogy, geognosy, chemistry, botany and physiology. Humboldt did his scientific investigations alongside his administrative and technical work. This raises the question of whether there were fruitful interactions between Humboldt's technical-administrative work and (parts of) his natural inquiry. I argue that the mining official Humboldt was a late eighteenth-century figure of hybrid savant-technician. Mines and smelting works provided numerous opportunities for studies of nature. Humboldt systematically used inspection tours for mineralogical and geognostic observations. He transformed mines into chemical laboratories, and he transferred knowledge and material items from his natural inquiries in mines to academic institutions. The main objective of this paper is to illuminate the persona of savant-technician (or scientific-technological expert) along with Humboldt's mixed technological and scientific work during his term as mining official. (shrink)

The generalised, automated reconstruction of the reasoning structures underlying persuasive communication is an enormously challenging task. While this work in argument mining is increasingly informed by the rich tradition of argumentation studies outside the computational field, the rhetorical perspective on argumentation is thus far largely ignored. To explore the application of rhetorical insights in argument mining, we conduct a pilot study on the connection between rhetorical figures and argumentation structure. Rhetorical figures are linguistic devices that perform a variety (...) of functions in argumentative discourse. The textual form of some of these figures is easy to identify automatically, such that an established connection between the figure and a preponderance of argumentative content would improve the performance of argument mining techniques. Furthermore, the automated mining of rhetorical figures could be used as an empirical, corpus-based testing ground for the claims made about these figures in the rhetorical literature. In the pilot study, we explore the connection between eight rhetorical figures the forms of which we expect to be relatively easy to identify computationally, and argumentation structure (concretely, we consider the six schemes ‘anadiplosis’, ‘epanaphora’, ‘epistrophe’, ‘epizeuxis’, ‘eutrepismus’, and ‘polyptoton’, and the two tropes ‘antithesis’ and ‘dirimens copulatio’, and relate their occurrences to relations of inference and conflict). The data of the study is collected in the MM2012c corpus of 39,694 words of argumentatively annotated transcripts from the BBC Radio 4’s Moral Maze discussion program. We show that some of the figures indeed correspond to passages of high argumentative density, relative to the text as a whole. (shrink)

Because obituaries are succinct and explicitly intended to summarize their subjects’ lives, they may be expected to include only the features that the author finds most salient but also to signal to others in the community the socially-recognized aspects of the deceased’s character. We begin by reviewing studies 1 and 2, in which obituaries were carefully read and labeled. We then report study 3, which further develops these results with a semi-automated, large-scale semantic analysis of several thousand obituaries. Geography, gender, (...) and elite status all turn out to be associated with the virtues and values associated with the deceased. (shrink)

In order to solve the problems of high investment and low box office losses in the film industry, this study analyzes the topic of film box office and film and television reviews based on social network big data. Firstly, the factors that affect the box office of the movie are analyzed. Secondly, continuous and discrete feature parts, text parts, and fusion parts are merged. The box office prediction model of mixed features using deep learning is established, and the movie (...) box office is predicted. Finally, compared with other algorithms and models, the box office prediction model of mixed features using deep learning is verified. The results show that compared with other models, the prediction accuracy of the mixed feature movie box office prediction model using depthwise separable convolution -Transformer is higher than that of other algorithm models. Its optimal mean square error value is 0.6549, and the optimal mean absolute error value is 0.1706. The constructed model predicts the box office of nine movies, and the error between the predicted value and the true value is about 10%. Therefore, the established movie box office prediction model has a good effect. This study can predict movies’ box office to reduce investment risk, so it is of great significance to movie investors and the social economy. (shrink)

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to (...) be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes. (shrink)

Modeling law search and retrieval as prediction problems has recently emerged as a predominant approach in law intelligence. Focusing on the law article retrieval task, we present a deep learning framework named LamBERTa, which is designed for civil-law codes, and specifically trained on the Italian civil code. To our knowledge, this is the first study proposing an advanced approach to law article prediction for the Italian legal system based on a BERT (Bidirectional Encoder Representations from Transformers) learning framework, which has (...) recently attracted increased attention among deep learning approaches, showing outstanding effectiveness in several natural language processing and learning tasks. We define LamBERTa models by fine-tuning an Italian pre-trained BERT on the Italian civil code or its portions, for law article retrieval as a classification task. One key aspect of our LamBERTa framework is that we conceived it to address an extreme classification scenario, which is characterized by a high number of classes, the few-shot learning problem, and the lack of test query benchmarks for Italian legal prediction tasks. To solve such issues, we define different methods for the unsupervised labeling of the law articles, which can in principle be applied to any law article code system. We provide insights into the explainability and interpretability of our LamBERTa models, and we present an extensive experimental analysis over query sets of different type, for single-label as well as multi-label evaluation tasks. Empirical evidence has shown the effectiveness of LamBERTa, and also its superiority against widely used deep-learning text classifiers and a few-shot learner conceived for an attribute-aware prediction task. (shrink)

This paper surveys ontological modeling of rhetorical concepts, developed for use in argument mining and other applications of computational rhetoric, projecting their future directions. We include ontological models of argument schemes applying Rhetorical Structure Theory (RST); the RhetFig proposal for modeling; the related RetFig Ontology of Rhetorical Figures for Serbian (developed by two of the authors); and the Lassoing Rhetoric project (developed by another of the authors). The Lassoing Rhetoric venture is interesting for its multifaceted approach to linguistic devices, (...) prominently including rhetorical figures, but also RST relations and stylistic models, like the use of historic present. This application takes a natural language text input and uses syntactic parsing tools to produce a knowledge base of linguistic entities using references to an OWL ontological framework, locating these devices using Semantic Web Rule Language (SWRL) logic rules. The paper also reports on a similar approach in research into detecting ironic tweets in a Serbian twitter corpus. The rhetorical schemes used for argument mining are also presented, as well as some suggestions for novel argument schemes based on the ontological approach to rhetorical figuration. (shrink)

At the heart of Mizrahi’s project lies a sociological narrative concerning the recent history of philosophers’ negative attitudes towards scientism. Critics (e.g. de Ridder (2019), Wilson (2019) and Bryant (2020)), have detected various empirical inadequacies in Mizrahi’s methodology for discussing these attitudes. Bryant (2020) points out one of the main pertinent methodological deficiencies here, namely that the mere appearance of the word ‘scientism’ in a text does not suffice in determining whether the author feels threatened by it. Not all (...) philosophers use the term in ‘inherently negative’ (29) or pejorative ways. In this paper, I not only corroborate Bryant’s critical claim, but argue that Mizrahi’s response to this part of Bryant’s objection is inadequate. (shrink)

This article reports a corpus-aided ecological discourse analysis of texts from an international mining company and an environmental advocacy group regarding a proposal to build a massive open-pit copper mine in the Santa Rita Mountains of Arizona, USA. The analysis details the grammatical and semantic clusters within the controversial environmental debate and how these clusters reflect the values and beliefs of each group as well as their conceptualization of the mountains and the environment. The integration of the ecolinguistic framework (...) with corpus linguistic methods of keyword analysis as well as part of speech and semantic tag analysis facilitated the identification of clusters of linguistic features perpetuating a dominant and ultimately destructive cultural discourse that places humans in a role of dominion and authority over the environment in the company texts. In contrast, clusters within the oppositional discourse forwarded by the environmental group promote the aesthetic value of the land and a need for responsible environmental stewardship. (shrink)

Pleasure, the defining object of kāmaśāstric scholarship, is harmonious sensory experience, the product of a “good fit” between the self and the world. It comes about when one moves in a world of fitting sense objects, and one has made oneself fit to enter that world. The bulk of kāmaśāstric literature is devoted to developing, enhancing, and enacting specific bodily and sensory capabilities in order to maximize one’s ability to affect and be affected by the world. This article examines the (...) model of subjectivity implied by kāmaśāstra, tracking two sorts of changes to the self assumed by this body of texts. First, it presents and analyzes the ways in which the social actors described in this literature come into being in and through the cultivation and performance of particular bodily and social acts; the “artifactual” nature of the self is presumed. Second, it considers the changing set of acts that are described and prescribed over the course of kāmaśāstric history, noting the increasing place of physiognomy and of magico-medical alterations of the body, and the elision of the social sphere of sensual activity. Finally, this article situates the kāmaśāstric model of subjectivity in relation to the larger śāstric project, drawing out the model of empiricism that it presupposes, and speculating upon the epistemological/ontological implications of kāmaśāstra as a science. (shrink)

In coal mining technology systems, it is very important to acquire, store, and represent basic geological data comprehensively and accurately. Based on the current working mode and information level in mining geology at coal mines, this paper proposes a process of building basic geological database for modeling of coal mines by using existing results’ data of mining geology and develops the efficient program for getting the basic geological data from the important 2D plane drawings’ achievement at mines, (...) such as the contour maps of mine coal seam floors, geological cross-sections, underground drilling results, and geological survey results, based on AutoLISP, which is a programming language for the secondary development of AutoCAD. The obtained data in general text format is stored and managed by the MongoDB database, which realizes the storage, query, analysis, and correction of massive data of geological objects in the space of the underground coalmine. The application results show that compared with the previous data acquisition methods such as manual input and graphic transformation attribute, the extraction of spatial and attribute data from the existing mine 2D plane drawings by programming can effectively avoid the prominent problems such as artificial gross error, distortion of graph conversion, and different database structure, make the obtained spatial geological data more comprehensive, accurate, and effective, and, meanwhile, increase the rate by more than 60%, which plays an important role in data support for the construction of the geological modeling systems for transparent mines. (shrink)

What is philosophy of science? Numerous manuals, anthologies or essays provide carefully reconstructed vantage points on the discipline that have been gained through expert and piecemeal historical analyses. In this paper, we address the question from a complementary perspective: we target the content of one major journal of the field—Philosophy of Science—and apply unsupervised text-mining methods to its complete corpus, from its start in 1934 until 2015. By running topic-modeling algorithms over the full-text corpus, we identified 126 (...) key research topics that span across 82 years. We also tracked their evolution and fluctuating significance over time in the journal articles. Our results concur with and document known and lesser-known episodes of the philosophy of science, including the rise and fall of logic and language-related topics, the relative stability of a metaphysical and ontological questioning (space and time, causation, natural kinds, realism), the significance of epistemological issues about the nature of scientific knowledge as well as the rise of a recent philosophy of biology and other trends. These analyses exemplify how computational text-mining methods can be used to provide an empirical large-scale and data-driven perspective on the history of philosophy of science that is complementary to other current historical approaches. (shrink)

Philosophy is often divided into two traditions: analytic and continental philosophy. Characterizing the analytic-continental divide, however, is no easy task. Some philosophers explain the divide in terms of the place of argument in these traditions. This raises the following questions: Is analytic philosophy rife with arguments while continental philosophy is devoid of arguments? Or can different types of arguments be found in analytic and continental philosophy? This paper presents the results of an empirical study of a large corpus of philosophical (...) texts mined from the JSTOR database (n = 53,260) designed to find patterns of argumentation by type. Overall, the results suggest that there are no significant differences between the types of arguments advanced in analytic and continental philosophy journal articles. The findings, therefore, provide no empirical support to the hypothesis that the divide between analytic and continental philosophy has to do with the place of argument in these traditions. (shrink)

The concept of “life” certainly is of some use to distinguish birds and beavers from water and stones. This pragmatic usefulness has led to its construal as a categorical predicate that can sift out living entities from non-living ones depending on their possessing specific properties—reproduction, metabolism, evolvability etc. In this paper, we argue against this binary construal of life. Using text-mining methods across over 30,000 scientific articles, we defend instead a degrees-of-life view and show how these methods can (...) contribute to experimental philosophy of science and concept explication. We apply topic-modeling algorithms to identify which specific properties are attributed to a target set of entities (bacteria, archaea, viruses, prions, plasmids, phages and the molecule of adenine). Eight major clusters of properties were identified together with their relative relevance for each target entity (two that relate to metabolism and catalysis, one to genetics, one to evolvability, one to structure, and—rather unexpectedly—three that concern interactions with the environment broadly construed). While aligning with intuitions—for instance about viruses being less alive than bacteria—these quantitative results also reveal differential degrees of performance that have so far remained elusive or overlooked. Taken together, these analyses provide a conceptual “lifeness space” that makes it possible to move away from a categorical construal of life by empirically assessing the relative lifeness of more-or-less alive entities. (shrink)

In this paper, we set out to investigate the following question: if science relies heavily on induction, does philosophy of science rely heavily on induction as well? Using data mining and text analysis methods, we study a large corpus of philosophical texts mined from the JSTOR database (n = 14,199) in order to answer this question empirically. If philosophy of science relies heavily on induction, just as science supposedly does, then we would expect to find significantly more inductive (...) arguments than deductive arguments and abductive arguments in the published works of philosophers of science. Using indicator words to classify arguments by type (namely, deductive, inductive, and abductive arguments), we search through our corpus to find patterns of argumentation. Overall, the results of our study suggest that philosophers of science do rely on inductive inference. But induction may not be as foundational to philosophy of science as it is thought to be for science, given that philosophers of science make significantly more deductive arguments than inductive arguments. Interestingly, our results also suggest that philosophers of science do not rely on abductive arguments all that much, even though philosophers of science consider abduction to be a cornerstone of scientific methodology. (shrink)

This release includes the code and supplementary information mentioned in: Malaterre, Christophe & Martin Léonard. 2023. "Charting the Territories of Epistemic Concepts in the Practice of Science: A Text-Mining Approach", British Journal for the Philosophy of Science.

The philosophical literature on scientific explanation contains a striking diversity of accounts. I use novel empirical methods to address this fragmentation and assess the importance and generality of explanation in science. My evidence base is a set of 781 articles from one year of the journal Science, and I begin by applying text mining techniques to discover patterns in the usage of “explain” and other words of philosophical interest. I then use random sampling from the data set to (...) develop and test a classification scheme for scientific explanation. My results show that explanation and inference to the best explanation are ubiquitous in science, that they occur across a wide range of scientific disciplines, and that they are a goal of scientific practise. These explanations and inferences to the best explanation come in a diversity forms, which at least partially justifies the fragmentation of philosophical accounts. I draw two methodological lessons: first that text mining can enhance traditional conceptual analysis by establishing facts about word usage; and second that random sampling of cases can increase our confidence that a philosophical account applies in general. These empirical techniques supplement traditional philosophical methods. (shrink)

In light of Taiwan's extensive exposure to the Chinese Communist Party's "cognitive domain infiltration warfare," this paper proposes new response mechanisms and strategies for cybersecurity and national defense. The focus is primarily on assessing the CCP's cognitive infiltration tactics to develop policy recommendations in cybersecurity linguistics. These recommendations are intended to serve as a reference for future national defense and information security policies. Within the constraints of limited resources, this study attempts to provide an integrated analysis method combining qualitative and (...) quantitative tools. This method involves text mining in cybersecurity linguistics, focusing on "textual content" and incorporating discourse deconstruction. The aim is to enhance the success rate in identifying and defining contentious information in cognitive warfare, thereby offering policy suggestions. -/- Keywords: Cognitive Infiltration, Big Data Corpus, Cybersecurity Linguistics, Text Mining Analysis, Narrative Language Deconstruction. (shrink)

Product recommendation system is realized by applying business rules acquired by data maining techniques. Business rules such as demographical patterns of purchase, are able to cover the groups of users that have a tendency to purchase products, but it is difficult to recommend products adaptive to various personal preferences only by utilizing them. In addition to that, it is very costly to gather the large volume of high quality survey data, which is necessary for good recommendation based on personal preference (...) model. A method collecting kansei information automatically without questionnaire survey is required. The constructing personal preference model from less favor data is also necessary, since it is costly for the user to input favor data. In this paper, we propose product recommendation system based on kansei information extracted by text mining and user's preference model constructed by Category-guided Adaptive Modeling, CAM for short. CAM is a feature construction method that can generate new features constructing the space where same labeled examples are close and different labeled examples are far away from some labeled examples. It is possible to construct personal preference model by CAM despite less information of likes and dislikes categories. In the system, retrieval agent gathers the products' specification and user agent manages preference model, user's likes and dislikes. Kansei information of the products is gained by applying text mining technique to the reputation documents about the products on the web site. We carry out some experimental studies to make sure that prefrence model obtained by our method performs effectively. (shrink)

This paper presents the results of an empirical study following up on Mizrahi (2021). Using the same methods of text mining and corpus analysis used by Mizrahi (2021), we test empirically a philosophical account of scientific progress that Mizrahi (2021) left out of his empirical study, namely, the so-called functional-internalist account of scientific progress according to which the aim or goal or scientific research is to solve problems. In general, our results do not lend much empirical evidence in (...) support of the problem-solving model of scientific progress over the other philosophical accounts of scientific progress (namely, the epistemic, noetic, and semantic accounts of scientific progress) tested in Mizrahi (2021) and in this follow-up study. Of all the subjects in the JSTOR database we have tested in this study, however, Mathematics is an interesting exception as far as the problem-solving model of scientific progress is concerned. For, in Mathematics alone, we have found that there is significantly more talk of the aims and/or goals of research in terms of solutions than in terms of truth, knowledge, or understanding. (shrink)