Recognizing sentence emotions based on polynomial kernel method using Ren-CECps

Abstract

Emotion recogonition on text has wide applications. In this study we propose a method of emotion recognition at sentence level based on a relative large emotion annotation corpus (Ren-CECps). From this corpus, we get the emotion lexicons for the eight basic emotions (expect, joy, love, surprise, anxiety, sorrow, angry and hate). Statistics show that the emotion lexicons derived from Ren-CECps are used more often in real use of language for emotional expressions than HOWNET sentimental lexicons. Kernel methods are state-of-the-art for solving machine learning problems. Polynomial kernel (PK) method is used to compute the similarities between sentences and the eight emotion lexicons. Then the experiential knowledge derived from Ren-CECps is used to recognize whether the eight emotion categories are present in a sentence. This method obtain 62.7% F-measure.

Emotion analysis in blogs at sentence level using a Chinese emotion corpus

“…The emotions of a simple sentence can be approximated by an addition by the word emotions whose emotion holder is the writer in this sentence. We also explored the features for word emotion recognition in sentences [21], and recognized sentence emotions based on polynomial kernel method [22]. Furthermore, in this study, we give more detail Table 1 Comparison of F-measure values of sentence emotions between simple and complex sentences with different number of emotional words Table 2 Comparison of F-measure values of sentence emotions between simple and complex sentences on the eight basic emotions analysis on sentence emotion recognition between simple and complex sentences.…”