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- Title
Multi-Level Fine-Scaled Sentiment Sensing with Ambivalence Handling.
- Authors
Wang, Zhaoxia; Ho, Seng-Beng; Cambria, Erik
- Abstract
Social media represent a rich source of information, such as critiques, feedback, and other opinions posted online by Internet users. Such information is typically a good reflection of users' sentiments and attitudes towards various services, topics, or products. Sentiment analysis has become an increasingly important natural language processing (NLP) task to help users make sense of what is happening in the Internet blogosphere and it can be useful for companies as well as public organizations. However, most existing sentiment analysis techniques are only able to analyze data at the aggregate level, merely providing a binary classification (positive vs. negative), and are not able to generate finer characterizations of sentiments as well as emotions involved. This paper describes a new opinion analysis scheme, i.e., a multi-level fine-scaled sentiment sensing with ambivalence handling. The ambivalence handler is presented in detail along with the strength-level tune parameters for analyzing the strength and the fine-scale of both positive or negative sentiments. It is capable of drilling deeper into text in order to reveal multi-level fine-scaled sentiments as well as different types of emotions.
- Subjects
AMBIVALENCE; NATURAL language processing; SENTIMENT analysis; ATTITUDE (Psychology); SOCIAL media; INTERNET users
- Publication
International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems, 2020, Vol 28, Issue 4, p683
- ISSN
0218-4885
- Publication type
Article
- DOI
10.1142/S0218488520500294