Automatically Constructing a Fine-Grained Sentiment Lexicon for Sentiment Analysis

Sentiment analysis is an important research area in natural language processing (NLP), and the performance of sentiment analysis models is largely influenced by the quality of sentiment lexicons. Existing sentiment lexicons contain only the sentiment information of words. In this paper, we propose an approach for automatically constructing a fine-grained sentiment lexicon that contains both emotion information and sentiment information to solve the problem that the emotion and sentiment of texts cannot be jointly analyzed. We design an emotion-sentiment transfer method and construct a fine-grained sentiment seed lexicon, and we then expand the sentiment seed lexicon by applying the graph dissemination method to the synonym set. Subsequently, we propose a multi-information fusion method based on neural network to expand the sentiment lexicon based on a corpus. Finally, we generate the Fine-Grained Sentiment Lexicon (FGSL), which contains 40,554 words. FGSL achieves F1 values of 61.97%, 69.58%, and 66.99% on three emotion datasets and 88.19%, 89.31%, and 86.88% on three sentiment datasets. Experimental results on multiple public benchmark datasets illustrate that FGSL achieves significantly better performance in both emotion analysis and sentiment analysis tasks.