Lexicon-based sentiment analysis for Kannada-English code-switch text

Abstract

Sentiment analysis is the process of computationally recognizing and classifying the attitudes conveyed in each text towards a particular topic, product, etc. which is either positive or negative. Sentiment analysis is one of the interesting applications of natural language processing (NLP) and which is used to analyze the social media. Text in social media is casual and it can be written either in code-switch or monolingual text. Several researchers have implemented sentiment analysis on monolingual text, though sentiments can be expressed in code-switch text. Sentiment analysis can be applied through deep learning (DL), machine learning (ML), or a Lexicon-based approach. Machine learning (ML) and deep learning (DL) methods are time-consuming, computationally expensive, and need training data for analysis. Lexicon-based method does not require training data and requires less time to find the sentiments in comparison with ML and DL. In this paper, we propose the Lexicon-based approach (NBLex) to analyze the sentiments expressed in Kannada-English code-switch text. This is the first effort that targets to perform sentiment analysis in Kannada-English code-switch text using the Lexicon-based approach. The proposed approach performed with better accuracy of 83.2% and 83% of F1-score.