Social media enables people to communicate with each other on the Internet in real-time and rich styles. In other words, there is a lot of information on the social media. If we can extract negative opinions of some brands, enterprises or politics, we can use these opinions to know the market demands and solve problems. In this paper, we propose a novel approach to extract negative-sentiment-oriented features and identify negative opinions in social media with text mining and machine learning techniques, support vector machine and neural network, as well as data collection with Web crawler. The experimental results show that our proposed methods can work effectively.
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This research was partially supported by Ministry of Science and Technology, Taiwan, under grant no. MOST 106-3114-E-009-008 and MOST 104-2811-E-009-050.
This article belongs to the Topical Collection: Special Issue on Social Media and Interactive Technologies
Guest Editors: Timothy K. Shih, Lin Hui, Somchoke Ruengittinun, and Qing Li
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Hsu, W., Hsu, H. & Tseng, V.S. Discovering negative comments by sentiment analysis on web forum. World Wide Web 22, 1297–1311 (2019). https://doi.org/10.1007/s11280-018-0561-6
- sentiment analysis
- text mining
- neural network
- support vector machine