Experiments on Hybrid Corpus-Based Sentiment Lexicon Acquisition

摘要

Numerous sentiment analysis applications make usage of a sentiment lexicon. In this paper we present experiments on hybrid sentiment lexicon acquisition. The approach is corpus-based and thus suitable for languages lacking general dictionary-based resources. The approach is a hybrid two-step process that combines semi-supervised graph-based algorithms and supervised models. We evaluate the performance on three tasks that capture different aspects of a sentiment lexicon: polarity ranking task, polarity regression task, and sentiment classification task. Extensive evaluation shows that the results are comparable to those of a well-known sentiment lexicon SentiWordNet on the polarity ranking task. On the sentiment classification task, the results are also comparable to SentiWordNet when restricted to monosen-timous (all senses carry the same sentiment) words. This is satisfactory, given the absence of explicit semantic relations between words in the corpus.