New features for sentiment analysis: do sentences matter?

Gezici, Gizem and Yanıkoğlu, Berrin and Tapucu, Dilek and Saygın, Yücel (2012) New features for sentiment analysis: do sentences matter? In: First International Workshop on Sentiment Discovery from Affective Data (SDAD 2012), Bristol, UK

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In this work, we propose and evaluate new features to be used in a word polarity based approach to sentiment classification. In particular, we analyze sentences as the first step before estimating the overall review polarity. We consider different aspects of sentences, such as length, purity, irrealis content, subjectivity, and position within the opinionated text. This analysis is then used to find sentences that may convey better information about the overall review polarity. The TripAdvisor dataset is used to evaluate the effect of sentence level features on polarity classification. Our initial results indicate a small improvement in classification accuracy when using the newly proposed features. However, the benefit of these features is not limited to improving sentiment classification accuracy since sentence level features can be used for other important tasks such as review summarization.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: sentiment analysis; sentiment classification; polarity detection; machine learning
Subjects: Q Science > QA Mathematics > QA075 Electronic computers. Computer science
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Berrin Yanıkoğlu
Date Deposited: 29 Nov 2012 14:17
Last Modified: 26 Apr 2022 09:07

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