A Frame Study on Sentiment Analysis of Hindi Language Using Machine Learning

Exploring Sentiment Analysis in Indian Regional Languages: Methods, Challenges, and Future Directions

“…This method has been applied to tackle various challenges in sentiment analysis, including named entity recognition, sarcasm detection, negation handling, and aspect-based analysis. The development and application of these lexicons have proven crucial in languages where machine learning resources may be scarce, highlighting the importance of linguistic and lexical resources in sentiment analysis [ 2,6,8,12,16,17,18,19,21,25,26,27,28,29,31,32].The phenomenon of code-mixing, where multiple languages are integrated within a single text, presents significant challenges for traditional NLP systems. This is particularly relevant in the Indian context, where bilingual or trilingual code-mixing is common.…”

“…Future work is suggested to focus on expanding the classification capabilities to include figurative language, enriching datasets with more diverse samples, exploring additional algorithms for enhanced accuracy, and further developing sentiment analysis models to accommodate low-resource languages. These directions underscore the evolving nature of sentiment analysis research and its critical role in understanding and leveraging user-generated content in multilingual societies [1,2,3,4,5,7,9,10,11,13,14,15,20,22,23,24,30,31,32].…”

“…Challenges noted include handling informal language and the scarcity of Hindi-specific NLP tools. Research also explores the creation of Hindi lexicons for lexicon-based sentiment analysis, emphasizing the importance of accurately capturing sentiments in a language with significant online user-generated content [1,2,3,9,10,20,28,32].In the Marathi context, researchers propose lexicon-based approaches to address the lack of computational resources for sentiment analysis. The development of sentiment lexicons, comprising positive and negative words, is suggested for classifying sentences.…”

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