Fuzzy-Rough Set Based Multi-labeled Emotion Intensity Analysis for Sentence, Paragraph and Document

Abstract

Most existing sentiment analysis methods focus on single-label classification, which means only a exclusive sentiment orientation (negative, positive or neutral) or an emotion state (joy, hate, love, sorrow, anxiety, surprise, anger, or expect) is considered for the given text. However, multiple emotions with different intensity may be coexisting in one document, one paragraph or even in one sentence. In this paper, we propose a fuzzy-rough set based approach to detect the multi-labeled emotions and calculate their corresponding intensities in social media text. Using the proposed fuzzy-rough set method, we can simultaneously model multi emotions and their intensities with sentiment words for a sentence, a paragraph, or a document. Experiments on a well-known blog emotion corpus show that our proposed multi-labeled emotion intensity analysis algorithm outperforms baseline methods by a large margin.

Project supported by National Natural Science Foundation of China (61370074, 61402091), the Fundamental Research Funds for the Central Universities of China under Grant N140404012.