Sentiment lexicon construction for Chinese book reviews based on ultrashort reviews

Abstract

Purpose Sentiment lexicon is an essential resource for sentiment analysis of user reviews. By far, there is still a lack of domain sentiment lexicon with large scale and high accuracy for Chinese book reviews. This paper aims to construct a large-scale sentiment lexicon based on the ultrashort reviews of Chinese books. Design/methodology/approach First, large-scale ultrashort reviews of Chinese books, whose length is no more than six Chinese characters, are collected and preprocessed as candidate sentiment words. Second, non-sentiment words are filtered out through certain rules, such as part of speech rules, context rules, feature word rules and user behaviour rules. Third, the relative frequency is used to select and judge the polarity of sentiment words. Finally, the performance of the sentiment lexicon is evaluated through experiments. Findings This paper proposes a method of sentiment lexicon construction based on ultrashort reviews and successfully builds one for Chinese books with nearly 40,000 words based on the Douban book. Originality/value Compared with the idea of constructing a sentiment lexicon based on a small number of reviews, the proposed method can give full play to the advantages of data scale to build a corpus. Moreover, different from the computer segmentation method, this method helps to avoid the problems caused by immature segmentation technology and an imperfect N-gram language model.

Design of Sentiment Analysis Framework of Digital Media Short Text Based on Multi-pattern Sentiment Lexicon

“…Zha et al designed and constructed a large SL of Chinese ultra-short comments to solve the lack of large-scale and high-precision SL in Chinese book reviews. This construction method solved the issues caused by immature segmentation techniques and imperfect language models [19]. Lijo et al put forward a rapid polarity detection method based on multiple lexical features, and built a SL with high expansivity and high polarity detection efficiency [9].…”

“…Luo et al proposed a text SA method integrated with neural networks for the instability of single emotion classification model in classification, which significantly improved the accuracy of text SA and effectively predicted the emotional polarity of text [10]. To solve the problem that time series in emotion is not taken into account in traditional text emotion analysis, Zhao et al proposed the method of fusion of window word vector and classifier in the decision-making level of short text emotion analysis, which had better performance [19].…”