A Comparative Study of Sentiment Analysis Techniques for Hindi Text: Machine Learning vs Deep Learning

Abstract

The increase in interest in sentiment analysis (SA) is presently supported by the demand for extracting information from texts, as seen in social media discussions and individual reviews. Languages like Hindi, however, are not easy to analyze for sentiment analysis because the morphology of these languages is complex and has its own syntax. This paper proposes a comparison of two popular sentiment analysis approaches for Hindi text: traditional machine learning (ML) approaches and more advanced deep learning (DL) approaches. We compare other ML algorithms, including Naive Bayes, Support Vector Machines (SVM), and Logistic Regression, with DL models such as Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and a specific application of an Artificial Neural Network for automating the measurement of kinematic characteristics of punches in boxing. Applied Sciences. Transformer-based BERT. With a dataset of sentiment analysis of the Hindi language, the research intends to determine the strengths and weaknesses of each of the methods. Our findings reveal that although deep learning algorithms, particularly BERT, are more accurate and possess a stronger contextualized understanding, the machine learning algorithm is computationally efficient. Such results have a significant implication regarding undertaking language sentiment analysis in languages whose morphology is rich, e.g., Hindi.