E-word of mouth sentiment analysis for user behavior studies. Issue 1 (January 2022)

• Record Type: Journal Article
• Authors: Li, Hui; Chen, Qi; Zhong, Zhaoman; Gong, Rongrong; Han, Guokai
• Is Part Of: Information processing & management. Volume 59:Issue 1(2022)
• Publication Date: 2022-01
• Subjects: sentiment analysis -- electronic word-of-mouth -- online dating service -- machine learning; 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.2021.102784
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: Induce and extract the emotion concept in each emotion review by introducing eight factors of sociological theory. Opinion mining and sentiment classification analysis are based on user behavior rather than product features. We propose a new classification of conceptual learning and natural language processes. The basic idea of our approach is to combine machine learning with dictionary dictionary to make multiple classifications of emotions in user behavior. Abstract: Nowadays, online word-of-mouth has an increasing impact on people's views and decisions, which has attracted many people's attention.The classification and sentiment analyse in online consumer reviews have attracted significant research concerns. In this thesis, we propose and implement a new method to study the extraction and classification of online dating services(ODS)'s comments. Different from traditional emotional analysis which mainly focuses on product attribution, we attempted to infer and extract the emotion concept of each emotional reviews by introducing social cognitive theory. In this study, we selected 4, 300 comments with extremely negative/positive emotions published on dating websites as a sample, and used three machine learning algorithms to analyze emotions. When testing and comparing the efficiency of user's behavior research, we use various sentiment analysis, machine learning techniques and dictionary-based sentiment analysis. We found that the combination of machine learning and lexicon-based method can achieve higher accuracy than any type of sentiment analysis. This research will provide a new perspective for the task of user behavior.