Which words are important?: an empirical study of Assamese sentiment analysis

Abstract

Sentiment analysis is an important research domain in text analytics and natural language processing. Since the last few decades, it has become a fascinating and salient area for researchers to understand human sentiment. According to the 2011 census, the Assamese language is spoken by 15 million people. Despite being a scheduled language of the Indian Constitution, it is still a resource-constrained language. Though it is an official language and presents its script, less work on sentiment analysis is reported in the Assamese language. In a linguistically diverse country like India, it is essential to provide a system to help people understand the sentiments in their native languages. So, the multilingual society in India would not be able to fully leverage the benefits of AI without the state-of-the-art NLP systems for the regional languages. Assamese language become popular due to its wide applications. Assamese users in social media as well as other platforms also are increasing day by day. Automatic sentiment analysis systems become effective for individuals, government, political parties, and other organizations and also can stop the negativity from spreading without a language divide. This paper presents a study on textual sentiment analysis using different lexical features of the Assamese news domain using machine learning and deep learning techniques. In the experiments, the baseline models are developed and compared against the models with lexical features. The proposed model with AAV lexical features based on XGBoost classifier predicts the highest accuracy of 86.76% with TF-IDF approach. It is observed that the combination of the lexical features with the machine learning classifier can significantly help the sentiment prediction in a small dataset scenario over the individual lexical features.