Sentiment Analysis in Hindi—A Survey on the State-of-the-art Techniques

Abstract

Sentiment Analysis (SA) has been a core interest in the field of text mining research, dealing with computational processing of sentiments, views, and subjective nature of the text. Due to the availability of extensive web-based data in Indian languages such as Hindi, Marathi, Kannada, Tamil, and so on. It has become extremely significant to analyze this data and recover valuable and relevant information. Hindi being the first language of the majority of the population in India, SA in Hindi has turned out to be a critical task particularly for companies and government organizations. This research portrays a systematic review specifically in the field of Hindi SA. The major contribution of this article includes the categorization of numerous articles based on techniques that have attracted researchers in performing SA tasks in Hindi language. This survey classifies these state-of-the-art computational intelligence techniques into four major categories namely lexicon-based techniques, machine learning techniques, deep learning techniques, and hybrid techniques. It discusses the importance of these techniques based on different aspects such as their impact on the issues of SA, levels of analysis, and performance evaluation measures. The research puts forward a comprehensive overview of the majority of the work done in Hindi SA. This study will help researchers in finding out resources such as annotated datasets, linguistic resources, and lexical resources. This survey delivers some significant findings and presents overall future research directions in the field of Hindi SA.

Sentiment Analysis in Low-Resource Settings: A Comprehensive Review of Approaches, Languages, and Data Sources

“…Sentiment classification methods are the methods used in categorizing text into polarities. They are classified into three categories: lexicon-based, machine learning (ML), and hybrid [12,24,[28][29][30][31][32][33][34][35]. However, the lexicon-based method relies on a predefined set of patterns, often referred to as a sentiment dictionary or lexicon, where each data entry is linked with a specific sentiment orientation [17,36].…”

“…Hybrid techniques combine both the lexicon and the ML methods. In general, these methods can be categorized into three main groups: supervised, semi-supervised [8], and unsupervised learning [21,34,[36][37][38].…”

Toward Machine Learning Based Binary Sentiment Classification of Movie Reviews for Resource Restraint Language (RRL)—Hindi

“…Assume that each review text is assigned to a set of sentiment polarity labels P = {pos, neg}. The review text classification develops a model whereby given a new review that r' / ∈ R, it will be assigned to a sentiment polarity label in P [3], [4].…”