Hindi Sentiment Analysis: A Comprehensive Review of Research and Its Implications

Abstract

Sentiment analysis in Hindi has become crucial for scholars, political analysts, and businesses due to the growing amount of Hindi content on digital platforms. This study discusses the unique challenges that the Hindi language shows, such as linguistic diversity, the use of both Devanagari and Roman scripts, and code-switching with English. Because of this complexity, effective sentiment classification requires specialized methods. This paper discusses a number of different approaches to analysing sentiment in Hindi, including lexicon-based approaches, deep learning strategies such as CNNs, LSTMs, and BERT, and machine learning models such as SVM and Naive Bayes approaches. To improve sentiment classification, a number of studies have used domain-specific dictionaries and Hindi-specific lexicons such as Hindi SentiwordNet (HSWN). Social media monitoring, consumer feedback analysis, and political sentiment analysis are among this research\'s beneficial applications. Despite these advancements, analysing code-mixed language and domain-specific sentiment remains challenging. Future research should focus on expanding Hindi lexicons to capture contextual and emotional subtleties, developing hybrid approaches that combine lexicon-based methods with deep learning, and creating specialized lexicons for politics, healthcare, and entertainment. Creating better Hindi and regional language sentiment analysis tools requires improving datasets and analytical approaches.

Introduction

The internet era has transformed how people express opinions, especially on social media, where vast amounts of multilingual data—including Hindi—are generated daily. Sentiment analysis is a computational technique used to classify text into positive, negative, or neutral sentiments. It is critical for understanding public opinion, guiding business, political, and social decisions.

Hindi, spoken by over 500 million people and written in Devanagari script, presents unique challenges for sentiment analysis due to its linguistic and cultural complexity, along with a shortage of annotated datasets. Despite the growth in digital content, Hindi sentiment analysis remains underexplored compared to other languages.

The field employs a range of techniques including lexicon-based, machine learning, and hybrid approaches. Hybrid models combining deep learning methods like CNN, LSTM, and optimization algorithms have achieved the highest accuracy (up to ~95.5%). Key steps involve data collection from various domains, preprocessing (e.g., tokenization, POS tagging, negation handling), feature extraction (Bag-of-Words, Word2Vec), and algorithm selection (Naïve Bayes, SVM, Random Forest, deep learning).

The literature shows a progression from traditional machine learning to advanced hybrid models. Challenges remain in handling code-mixed text, context, sarcasm, and domain-specific language. Continued research on Hindi-specific datasets and linguistic nuances is essential for improving sentiment analysis in this significant language.

Conclusion

Hindi text sentiment analysis is vital for understanding India\'s complex linguistic landscape. NLP and machine learning have improved sentiment classification, but Hindi\'s complex syntax, frequent code-switching with English, and scarcity of annotated datasets remain problems. In order to address these challenges, linguistic-based, machine learning, and deep learning models have been developed. Expanding datasets, addressing context-specific nuances, and processing mixed-language content should be priorities. Sentiment analysis outside English is essential for inclusive technological development and accurately reflecting India\'s large Hindi-speaking population, benefiting social media monitoring, market research, and policymaking.

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Copyright

Copyright © 2025 Shalini V. Sathe, Abhijeet Dhepe, Sagar Pravin Chitte, Dr. Sunil S. Nimbhore. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.