Sentiment Analysis and Emotion Classification using Machine Learning Techniques for Nagamese Language -- A Low-resource Language

TL;DR

This paper presents the first effort to perform sentiment analysis and emotion classification on Nagamese, a low-resource creole language, using machine learning techniques with a custom sentiment lexicon.

Contribution

It introduces a sentiment polarity lexicon for Nagamese and applies supervised machine learning methods for sentiment and emotion detection in this low-resource language.

Findings

Achieved initial sentiment classification results for Nagamese

Developed a 1,195-word sentiment lexicon for Nagamese

Demonstrated the feasibility of machine learning for low-resource language sentiment analysis

Abstract

The Nagamese language, a.k.a Naga Pidgin, is an Assamese-lexified creole language developed primarily as a means of communication in trade between the people from Nagaland and people from Assam in the north-east India. Substantial amount of work in sentiment analysis has been done for resource-rich languages like English, Hindi, etc. However, no work has been done in Nagamese language. To the best of our knowledge, this is the first attempt on sentiment analysis and emotion classification for the Nagamese Language. The aim of this work is to detect sentiments in terms of polarity (positive, negative and neutral) and basic emotions contained in textual content of Nagamese language. We build sentiment polarity lexicon of 1,195 nagamese words and use these to build features along with additional features for supervised machine learning techniques using Na"ive Bayes and Support Vector Machines. Keywords: Nagamese, NLP, sentiment analysis, machine learning