Sentiment Lexicon Construction with Hierarchical Supervision Topic Model

In this paper, we propose a novel hierarchical supervision topic model to construct a topic-adaptive sentiment lexicon (TaSL) for higher-level classification tasks. It is widely recognized that sentiment lexicon as a useful prior knowledge is crucial in sentiment analysis or opinion mining. However, many existing sentiment lexicons are constructed ignoring the variability of the sentiment polarities of words in different topics or domains. For example, the word 'amazing' can refer to causing great surprise or wonder but can also refer to very impressive and excellent. In TaSL, we solve this issue by jointly considering the topics and sentiments of words. Documents are represented by multiple pairs of topics and sentiments, where each pair is characterized by a multinomial distribution over words. Meanwhile, this generating process is supervised under hierarchical supervision information of documents and words. The main advantage of TaSL is that the sentiment polarity of each word in different topics can be sufficiently captured. This model is beneficial to construct a domain-specific sentiment lexicon and then effectively improve the performance of sentiment classification. Extensive experimental results on four publicly available datasets, MR, OMD, semEval13A, and semEval16B were presented to demonstrate the usefulness of the proposed approach. The results have shown that TaSL performs better than the existing manual sentiment lexicon (MPQA), the topic model based domain-specific lexicon (ssLDA), the expanded lexicons(Weka-ED, Weka-STS, NRC, Liu's), and deep neural network based lexicons (nnLexicon, HIT, HSSWE).