Computing Attitude and Affect in Text: Theory and Applications /
edited by James G. Shanahan, Yan Qu, Janyce Wiebe.
- xvI, 341 páginas recurso en línea.
- The Information Retrieval Series, 20 1387-5264 ; .
Springer eBooks
Contextual Valence Shifters -- Conveying Attitude with Reported Speech -- Where Attitudinal Expressions Get their Attitude -- Analysis of Linguistic Features Associated with Point of View for Generating Stylistically Appropriate Text -- The Subjectivity of Lexical Cohesion in Text -- A Weighted Referential Activity Dictionary -- Certainty Identification in Texts: Categorization Model and Manual Tagging Results -- Evaluating an Opinion Annotation Scheme Using a New Multi-Perspective Question and Answer Corpus -- Validating the Coverage of Lexical Resources for Affect Analysis and Automatically Classifying New Words along Semantic Axes -- A Computational Semantic Lexicon of French Verbs of Emotion -- Extracting Opinion Propositions and Opinion Holders using Syntactic and Lexical Cues -- Approaches for Automatically Tagging Affect: Steps Toward an Effective and Efficient Tool -- Argumentative Zoning for Improved Citation Indexing -- Politeness and Bias in Dialogue Summarization: Two Exploratory Studies -- Generating More-Positive and More-Negative Text -- Identifying Interpersonal Distance using Systemic Features -- Corpus-Based Study of Scientific Methodology: Comparing the Historical and Experimental Sciences -- Argumentative Zoning Applied to Critiquing Novices’ Scientific Abstracts -- Using Hedges to Classify Citations in Scientific Articles -- Towards a Robust Metric of Polarity -- Characterizing Buzz and Sentiment in Internet Sources: Linguistic Summaries and Predictive Behaviors -- Good News or Bad News? Let the Market Decide -- Opinion Polarity Identification of Movie Reviews -- Multi-Document Viewpoint Summarization Focused on Facts, Opinion and Knowledge.
The chapters in this book address attitude, affect, and subjective opinion. Various conceptual models and computational methods are presented, including distinguishing attitudes from simple factual assertions; distinguishing between the author’s reports from reports of other people’s opinions; and distinguishing between explicitly and implicitly stated attitudes. In addition, many applications are described that promise to benefit from the ability to understand attitudes and affect, such as indexing and retrieval of documents by opinion; automatic question answering about opinions; analysis of sentiment in the media and in discussion groups; analyzing client discourse in therapy and counseling; determining relations between scientific texts; generating more appropriate texts; and creating writers’ aids. In addition to English texts, the collection includes studies of French, Japanese, and Portuguese texts. The chapters are extended and revised versions of papers presented at the American Association for Artificial Intelligence (AAAI) Spring Symposium on Exploring Attitude and Affect in Text, which took place in March 2004 at Stanford University. The symposium, and the book which grew out it, represent a first foray into this area and a balance among conceptual models, computational methods, and applications.