Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis

Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis Stefanos Angelidis and Mirella Lapata Institute for Language, Cognition and Computation School of Informatics, University of Edinburgh 10 Crichton Street, Edinburgh EH8 9AB s.angelidis@ed.ac.uk, mlap@inf.ed.ac.uk Abstract We consider the task of fine-grained senti- ment analysis from the perspective of multi- ple instance learning (MIL). Our neural model is trained on document sentiment labels, and learns to predict the sentiment of text seg- ments, i.e. sentences or elementary discourse units (EDUs), without segment-level supervi- sion. We introduce an attention-based polar- ity scoring method for identifying positive and negative text snippets and a new dataset which we call SPOT (as shorthand for Segment-level POlariTy annotations) for evaluating MIL- style sentiment models like ours. Experimen- tal results demonstrate superior performance against multiple baselines, whereas a judge- ment elicitation study shows that EDU-level opinion extraction produces more informative summaries than sentence-based alternatives. 1 Introduction Sentiment analysis has become a fundamental area of research in Natural Language Processing thanks to the proliferation of user-generated content in the form of online reviews, blogs, internet forums, and social media. A plethora of methods have been pro- posed in the literature that attempt to distill senti- ment information from text, allowing users and ser- vice providers to make opinion-driven decisions. The success of neural networks in a variety of ap- plications (Bahdanau et al., 2015; Le and Mikolov, 2014; Socher et al., 2013) and the availability of large amounts of labeled data have led to an in- creased focus on sentiment classification. Super- vised models are typically trained on documents (Johnson and Zhang, 2015a; Johnson and Zhang, 2015b; Tang et al., 2015; Yang et al., 2016), sen- tences (Kim, 2014), or phrases (Socher et al., 2011; [Rating: ⋆⋆] I had a very mixed experience at The Stand. The burger and fries were good. The chocolate shake was divine: rich and creamy. The drive-thru was horrible. It took us at least 30 minutes to order when there were only four cars in front of us. We complained about the wait and got a half–hearted apology. I would go back because the food is good, but my only hesitation is the wait. Summary + The burger and fries were good + The chocolate shake was divine + I would go back because the food is good – The drive-thru was horrible – It took us at least 30 minutes to order Figure 1: An EDU-based summary of a 2-out-of-5 star review with positive and negative snippets. Socher et al., 2013) annotated with sentiment la- bels and used to predict sentiment in unseen texts. Coarse-grained document-level annotations are rel- atively easy to obtain due to the widespread use of opinion grading interfaces (e.g., star ratings ac- companying reviews). In contrast, the acquisition of sentence- or phrase-level sentiment labels re- mains a laborious and expensive endeavor despite its relevance to various opinion mining applica- tions, e.g., detecting or summarizing consumer opin- ions in online product reviews. The usefulness of finer-grained sentiment analysis is illustrated …

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