Plutchik Wheel of Emotion and Machine Learning-Based Hybrid Sentiment Analysis for the Hindi Language with Minimum Dependency on High Computation Resources

Sentiment analysis is a natural language processing technique for extracting sentimental, opinion, or emotional information from text data. Sentiment analysis of natural language is a complex task that requires high computation resources, and Sentiment analysis becomes more complicated if the language of text data is Hindi. This manuscript proposes a plutchik wheel of emotion and Machine learning-based hybrid sentiment analysis approach for the Hindi language with minimum dependency on high computation resources. In the proposed methodology, we assembled a lexicon for the Hindi language. Each word of the lexicon is mapped with respected plutchik emotion. As very few standardised data sets are available for the sentiment analysis of the Hindi language, thus we drafted a new data set. The data set compiled is a collection of educational kid stories from different publication books. Our experiment study shows that the proposed approach gives 73.45% accuracy on this data set with a lexicon comparable to state-of-the-art machine learning and lexicon-based hybrid sentiment analysis approaches. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.