Thumbs up?: sentiment classification using machine learning techniques
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Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
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Clues for detecting irony in user-generated contents: oh...!! it's "so easy" ;-)
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
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Liars and saviors in a sentiment annotated corpus of comments to political debates
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We propose and evaluate a method for automatically creating a reference corpus for training text classification procedures for mining political opinions in user-generated content. The process starts by compiling a collection of highly opinionated comments posted by users on an on-line newspaper. Then, we define and use a set of manually-crafted high-precision rules supported by a large sentiment-lexicon in order to identify sentences in each comment expressing opinions about political entities. Finally, the opinions found are propagated to the remainder sentences of the comment mentioning the same entities, thus increasing the number and variety of opinion-bearing sentences. Results show that most of the rules can identify negative opinions with very high precision, and these can be safely propagated to the remainder sentences in the comment in almost 100% of the cases. Due to problems arising from irony, the precision of identification drops for positive opinions, but several rules still reach high precision. Propagation of positive opinions is correct in about 77% of the cases, and most errors at this stage result from irony and polarity inversion throughout the comment.