Abstract: One of the crucial fields in the contemporary technology world is sentiment analysis. Sentiment analysis, a branch of text mining, is the study of extracting feelings and emotions from actual data. Textual sentiment analysis can be helpful for many different decision-making procedures. Social media comments, on the other hand, frequently use scripts that are not their own and do not strictly adhere to grammar requirements. Hinglish, a language that combines Hindi and English, is widely used as an informal writing language in India. based on aspects In the realm of artificial intelligence, sentiment analysis from Hinglish text is a difficult challenge to solve.

The most current advances in sentiment analysis from English text, code mixed text, and other issues associated with the same have been described in this work. The categorization of aspect-based emotion is the primary difficulty faced by the Hinglish text model. Therefore, it is crucial to select the right categorization model. This study reviews prior work and describes many sorts of aspects of emotion text data and the extraction methods related to them. Analyses of the reliability of several classification methods were also conducted. Additionally, language and textual characteristics of several ML approaches were analysed for actual word analysis.

 

| DOI: 10.17148/IARJSET.2023.10130