WKWSCI Sentiment Lexicon v1.1 available for download

A general-purpose English sentiment lexicon called WKWSCI Sentiment Lexicon v1.1 (named after the authors’ school Wee Kim Wee School of Communication & Information) is available for download. The lexicon is based on the 12dicts common American English word lists compiled by Alan Beale from twelve source dictionaries. The lexicon contains 29,729 words tagged with 4 parts-of-speech: adjective, adverb, noun, and verb. The lexicon comprises 3,187 positive words, 7,247 negative words and 19,295 neutral words. WKWSCI Sentiment Lexicon v1.0 was described and compared with five existing lexicons in the paper “Lexicon-Based Sentiment Analysis: Comparative Evaluation of Six Sentiment Lexicons“. Version 1.1 includes some improvements resulting from the reported study....
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Article: Sentiment analysis of online news text: A case study of appraisal theory

Khoo, C.S.G., Nourbakhsh, A., & Na, J.C. (2012). Sentiment analysis of online news text: A case study of appraisal theory. Online Information Review, 36(6), 858-878.  Abstract Purpose. Sentiment analysis and emotion processing are attracting increasing interest in many fields. Computer and information scientists are developing automated methods for sentiment analysis of online text. Most of the research have focused on identifying sentiment polarity or orientation—whether a document, usually product or movie review, carries a positive or negative sentiment. It is time for researchers to address more sophisticated kinds of sentiment analysis. This paper evaluates a particular linguistic framework called appraisal theory for adoption in manual as well as automatic sentiment analysis of news text. Method. The appraisal theory is applied to the analysis of a sample of political news articles reporting on Iraq and economic policies of George W. Bush and Mahmoud Ahmadinejad to assess its utility and to...
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Article: Design and development of a concept-based multi-document summarization system for research abstracts

Ou, S., Khoo, C.S.G., & Goh, D. (2008). Design and development of a concept-based multi-document summarization system for research abstracts. Journal of Information Science, 34(3), 308-326. Summary: This paper describes a concept-based multi-document summarization system that was developed to summarize sets of dissertation abstracts in sociology that might be retrieved by an information retrieval system or Web search engine in response to a user query. The summarization method developed in this study is a hybrid method comprising four major steps: Macro-level discourse parsing: An automatic discourse parsing method was developed to segment a dissertation abstract into several macro-level sections and identify which sections contain important research information; Information extraction: An information extraction method was developed to extract research concepts and relationships as well as other kinds of information from the micro-level structure (within sentences); Information integration: An information integration method was developed to integrate similar concepts and relationships...
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Article: Automatic multi-document summarization of research abstracts: Design and user evaluation

Ou, S., Khoo, C.S.G., & Goh, D. (2007). Automatic multi-document summarization of research abstracts: Design and user evaluation. Journal of the American Society for Information Science & Technology, 58(10), 1419-1435. Summary: This study developed a method for multi-document summarization of sociology dissertation abstracts. We did not use traditional sentence extraction approaches. Instead, a hybrid summarization method involving both extractive and abstractive techniques was used. This method focused on extracting and integrating similarities and differences across different documents to summarize a set of related documents. The identification of similarities and differences was based more on identifying research concepts and relationships expressed in the text, rather than words, phrases or sentences and rhetorical relations used in previous studies. To do that, the macro-level discourse structure (between sentences and segments) peculiar to sociology dissertation abstracts was analyzed to identify which segments of the text contain more important research information. Then the micro-level...
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