Journal
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
Volume 23, Issue 11, Pages 1750-1761Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TASLP.2015.2449071
Keywords
Artificial intelligence; joint segmentation and classification; natural language processing; sentiment analysis; sentiment classification
Categories
Funding
- National High Technology Development 863 Program of China [2015AA015407]
- National Natural Science Foundation of China [61133012, 61273321]
- Baidu Fellowship
- IBM Ph.D. Fellowship
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In this paper, we propose a joint segmentation and classification framework for sentence-level sentiment classification. It is widely recognized that phrasal information is crucial for sentiment classification. However, existing sentiment classification algorithms typically split a sentence as a word sequence, which does not effectively handle the inconsistent sentiment polarity between a phrase and the words it contains, such as {not bad, bad} and {a great deal of, great}. We address this issue by developing a joint framework for sentence-level sentiment classification. It simultaneously generates useful segmentations and predicts sentence-level polarity based on the segmentation results. Specifically, we develop a candidate generation model to produce segmentation candidates of a sentence; a segmentation ranking model to score the usefulness of a segmentation candidate for sentiment classification; and a classification model for predicting the sentiment polarity of a segmentation. We train the joint framework directly from sentences annotated with only sentiment polarity, without using any syntactic or sentiment annotations in segmentation level. We conduct experiments for sentiment classification on two benchmark datasets: a tweet dataset and a review dataset. Experimental results show that: 1) our method performs comparably with state-of-the-art methods on both datasets; 2) joint modeling segmentation and classification outperforms pipelined baseline methods in various experimental settings.
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