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Aspect Sentiment Classification with both Word-level and Clause-level AttentionNetworks

Aspect sentiment classification, a challenging taskin sentiment analysis, has been attracting more andmore attention in recent years. In this paper, wehighlight the need for incorporating the importancedegrees of both words and clauses inside a sentenceand propose a hierarchical network with both word-level and clause-level attentions to aspect senti-ment classification.Specifically, we first adoptsentence-level discourse segmentationto segmenta sentence into several clauses. Then, we lever-age multiple Bi-directional LSTM layers to encodeall clauses and propose a word-level attention layerto capture the importance degrees of words in eachclause. Third and finally, we leverage another Bi-directional LSTM layer to encode the output fromthe former layers and propose a clause-level atten-tion layer to capture the importance degrees of allthe clauses inside a sentence. Experimental re-sults on thelaptopandrestaurantdatasets fromSemEval-2015 demonstrate the effectiveness of ourproposed approach to aspect sentiment classifica-tion.

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