Publication Type

Journal Article

Version

acceptedVersion

Publication Date

2-2020

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.

Keywords

Ambivalence sentiment handling, emotion sensing, multi-level fine-scaled sentiment analysis, sentiment strength level, social media analysis

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Data Science and Engineering

Publication

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

Volume

28

Issue

4

First Page

683

Last Page

697

Identifier

10.1142/S0218488520500294

Additional URL

https://doi.org/10.1142/S0218488520500294

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