WikiSent: Weakly supervised sentiment analysis through extractive summarization with Wikipedia
|WikiSent: Weakly supervised sentiment analysis through extractive summarization with Wikipedia|
|Author(s)||Mukherjee S., Bhattacharyya P.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Information Extraction, Reviews, Sentiment Analysis, Summarization, Text mining, Weakly Supervised System, Wikipedia (Extra: Information Extraction, Sentiment analysis, Summarization, Text mining, Weakly Supervised System, Wikipedia, Data mining, Learning systems, Natural language processing systems, Reviews, Websites, Motion pictures)|
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WikiSent: Weakly supervised sentiment analysis through extractive summarization with Wikipedia is a 2012 conference paper written in English by Mukherjee S., Bhattacharyya P. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
This paper describes a weakly supervised system for sentiment analysis in the movie review domain. The objective is to classify a movie review into a polarity class, positive or negative, based on those sentences bearing opinion on the movie alone, leaving out other irrelevant text. Wikipedia incorporates the world knowledge of movie-specific features in the system which is used to obtain an extractive summary of the review, consisting of the reviewer's opinions about the specific aspects of the movie. This filters out the concepts which are irrelevant or objective with respect to the given movie. The proposed system, WikiSent, does not require any labeled data for training. It achieves a better or comparable accuracy to the existing semi-supervised and unsupervised systems in the domain, on the same dataset. We also perform a general movie review trend analysis using WikiSent.
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