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.
Shalini V. Sathe (Wed,) studied this question.