A Comprehensive Review of Sentiment Analysis on Indian Regional Languages: Techniques, Challenges, and Trends

Sentiment analysis (SA) is the process of understanding emotion within a text. It helps identify the opinion, attitude, and tone of a text categorizing it into positive, negative, or neutral. SA is frequently used today as more and more people get a chance to put out their thoughts due to the advent of social media. Sentiment analysis benefits industries around the globe, like finance, advertising, marketing, travel, hospitality, etc. Although the majority of work done in this field is on global languages like English, in recent years, the importance of SA in local languages has also been widely recognized. This has led to considerable research in the analysis of Indian regional languages. This paper comprehensively reviews SA in the following major Indian Regional languages: Marathi, Hindi, Tamil, Telugu, Malayalam, Bengali, Gujarati, and Urdu. Furthermore, this paper presents techniques, challenges, findings, recent research trends, and future scope for enhancing results accuracy

The Today Tendency of Sentiment Classification

Sentiment classification has already been studied for many years because it has had many crucial contributions to many different fields in everyday life, such as in political activities, commodity production, and commercial activities. There have been many kinds of the sentiment analysis such as machine learning approaches, lexicon-based approaches, etc., for many years. The today tendency of the sentiment classification is as follows: (1) Processing many big data sets with shortening execution times (2) Having a high accuracy (3) Integrating flexibly and easily into many small machines or many different approaches. We will present each category in more details

Advancements in Machine Learning for Robust Sentiment Analysis of Consumer Product Reviews

In this age of social media and huge data, sentiment analysis has become an essential activity. In order for businesses to measure consumer happiness and make educated decisions, it is crucial to understand the feelings conveyed in product reviews. The purpose of this study is to simulate and evaluate an enhanced machine learning approach to product review sentiment analysis. The goal is to create a powerful model for sentiment analysis that can beat current methods in terms of efficiency and accuracy. In this paper, we present a new approach to sentiment analysis in product reviews by integrating state-of-the-art feature extraction with sentiment classification algorithms and model optimization techniques. We begin by outlining the significance of sentiment analysis and the difficulties encountered by current approaches. Additionally, it specifies the aims and parameters of this study. The section on similar studies provides a thorough analysis of the literature and draws attention to the shortcomings of previous methods. In the methodology part, we lay out the specifics of our improved machine learning strategy and the thinking behind the methods we chose. In the results analysis, we test how well our model does on a variety of product review datasets. We compare our results to those of baseline models and cutting-edge sentiment analysis systems, and we provide the accuracy, precision, recall, and F1-score measures. Our discussion also covers the model's ability to handle different kinds of items and reviews. In comparison to more conventional approaches, our study shows that sentiment analysis is far more accurate. To demonstrate the model's efficacy in various contexts and to highlight its flaws, we use tables and graphs. At the end of the study, we go over some of the possible business uses, suggestions for further studies, and consequences of our results. In sum, this study aids in the development of sentiment analysis methods and gives a great resource for companies who want to learn more about how customers feel about their products through reviews.

A review of sentiment analysis research in Arabic language

Sentiment analysis is a task of natural language processing which has recently attracted increasing attention. However, sentiment analysis research has mainly been carried out for the English language. Although Arabic is ramping up as one of the most used languages on the Internet, only a few studies have focused on Arabic sentiment analysis so far. In this paper, we carry out an in-depth qualitative study of the most important research works in this context by presenting limits and strengths of existing approaches. In particular, we survey both approaches that leverage machine translation or transfer learning to adapt English resources to Arabic and approaches that stem directly from the Arabic language