HEMOS: A novel deep learning-based fine-grained humor detecting method for sentiment analysis of social media. Issue 6 (November 2020)

• Record Type: Journal Article
• Authors: Li, Da; Rzepka, Rafal; Ptaszynski, Michal; Araki, Kenji
• Is Part Of: Information processing & management. Volume 57:Issue 6(2020:Nov.)
• Publication Date: 2020-11
• Subjects: Sentiment analysis -- Humor polarity -- Social media -- Emoji -- Deep learning; 00-01 -- 99-00; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2020.102290
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: We collected 576 frequent Chinese Internet slang expressions as a Chinese slang lexicon. We converted the 109 Weibo emojis into textual features creating Chinese emoji lexicon. We empirically confirmed inherent humor characteristic to Chinese culture visible on Weibo and divided Weibo posts into four categories. We proposed HEMOS, a fine-grained humor detecting method for sentiment analysis of social media. Abstract: In this paper we introduce HEMOS (H umor-EMO ji-S lang-based) system for fine-grained sentiment classification for the Chinese language using deep learning approach. We investigate the importance of recognizing the influence of humor, pictograms and slang on the task of affective processing of the social media. In the first step, we collected 576 frequent Internet slang expressions as a slang lexicon; then, we converted 109 Weibo emojis into textual features creating a Chinese emoji lexicon. In the next step, by performing two polarity annotations with new "optimistic humorous type" and "pessimistic humorous type" added to standard "positive" and "negative" sentiment categories, we applied both lexicons to attention-based bi-directional long short-term memory recurrent neural network (AttBiLSTM) and tested its performance on undersized labeled data. Our experimental results show that the proposed method can significantly improve the state-of-the-art methods in predicting sentiment polarity on Weibo, the largest Chinese social network.