DoSLex: automatic generation of all domain semantically rich sentiment lexicon

Abstract

For sentiment analysis, lexicons are among the important resources. Existing sentiment lexicons have a generic polarity for each word. In fact, many words have different polarities when they are used in different domain. For the first time, in this work automation of a domain-specific sentiment lexicon named “DoSLex” has been proposed. In DoSLex, all the words are represented in a circle where the centre stands for the domain, and the x and y axis for the strength and the orientation of the sentiment, respectively. In the circle, the radius is the contextual similarity between the domain and term calculated using MuRIL embeddings, and the angle is the prior sentiment score taken from various knowledge bases. The proposed approach is language-independent and can be applied to any domain. The extensive experiments were conducted on three low-resource languages: Hindi, Tamil, and Bangla. The experimental studies discuss the performance of the combinations of different word embeddings (FastText, M-Bert and MuRIL) with several sources of prior sentiment knowledge bases on various domains. The performance of DoSLex has also been compared with three sentiment lexicons, and the results demonstrating a significant improvement in sentiment analysis.