|Title||Beyond Positive/Negative Classification: Automatic Extraction of Sentiment Clues from Microblogs|
|Year of Publication||2011|
|Authors||Lu Chen, Wenbo Wang, Meenakshi Nagarajan, Shaojun Wang, Amit Sheth|
|Keywords||Optimization and Opinion Mining and Sentiment Analysis and Sentiment Extraction|
Microblogging provides a large volume of text for learning and understanding people's sentiments on a variety of topics. Much of the current work on sentiment analysis of microblogs (e.g., tweets) focuses on document level polarity. However, identifying sentiment clues with respect to specific targets (e.g., named entities) can be more useful than pure document polarity results. For example, sentiment clues such as 'must see', 'awesome', 'rate 5 stars' (in the movie domain) are much more meaningful than the polarities of tweets only. Previous attempts at single-word sentiment clue extraction from formal text will not suffice for extracting multi-word sentiment phrases. Single words 'must' and 'see' do not separately convey polarity, but their combination 'must see' expresses strong positive sentiment towards a movie target. Another issue with identifying sentiment clues is identifying informal sentiment expressions, such as misspellings ('kool'), abbreviations ('wtf') and slangs ('da bomb'). In this paper, we propose an approach for automatically extracting both single-word and multi-word sentiment clues. Such clues can include both traditional and slang expressions. We also present a mechanism for assessing their target-specific polarities from an unlabeled microblog corpus. Our approach first leverages traditional and slang subjective lexicons to generate candidate sentiment clues given some specific target. It then incorporates inter-clue relations from corpora into an optimization model to estimate the probability of a clue denoting positive/negative sentiment. Experiments using microblog data sets on two different domains -- movie and person -- show that the proposed approach can effectively 1) extract single-word as well as phrase sentiment clues, 2) identify both traditional and slang sentiment clues, and 3) determine their target-specific polarities. We also demonstrate how the proposed approach is superior in comparison with several baseline methods.
|Full Text|| |
Lu Chen, Wenbo Wang, Meenakshi Nagarajan, Shaojun Wang and Amit P. Sheth. Beyond Positive/Negative Classiﬁcation: Automatic Extraction of Sentiment Clues from Microblogs. Kno.e.sis Center Technical Report 2011.