E-word of mouth sentiment analysis for user behavior studies
| Autor: | Qi Chen, Rongrong Gong, Zhaoman Zhong, Hui Li, Guokai Han |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Computer science
business.industry Perspective (graphical) Sentiment analysis Word of mouth Sample (statistics) Library and Information Sciences Management Science and Operations Research computer.software_genre Computer Science Applications Task (project management) Media Technology Artificial intelligence Product (category theory) Attribution business computer Social cognitive theory Natural language processing Information Systems |
| Zdroj: | Information Processing & Management. 59:102784 |
| ISSN: | 0306-4573 |
| DOI: | 10.1016/j.ipm.2021.102784 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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