Machine Learning Techniques for Sentiment Analysis of Indian Languages

Abstract

Sentiment Analysis is the domain of automatically understanding the emotions, feelings, opinions in a textual data. It is a way of understating how a product, brand, service, idea or an event is viewed by common people, customers and stakeholders. Sentiment Analysis Systems are used by politicians, business leaders, developers and researchers to infer useful information as per their specific needs. It is used in business decision making process to value the views of the customers. Sentiment analysis has become a hot topic of scientific and market research in the field of natural Language Processing. India is a large populated country and the number of Internet users is also huge. Most people share their experience in English. However, during the last decade, due to the accessibility of Internet and evolution in language modelling people express their views in their own native Indian language. With the increase in Indian language text, researchers find it quite fascinating to infer valuable information from this unstructured text data. A number of machine learning techniques have been applied on this textual data set. Basic concepts of Sentiment analysis shall be discussed with focus on Indian language text in this paper. Due to on availability of rich lexicon resources for unsupervised learning techniques and better evaluation measures for the Supervised learning techniques, the later become the first choice for researchers in the field of Natural Language Processing. A comparative analysis shall be made for various supervised machine learning techniques in the context of Indian languages.