Sentimental analysis in voting involves using natural language processing (NLP) techniques to evaluate and interpret the sentiments, emotions, or opinions expressed by voters in various forms of text,
Sentimental analysis in voting refers to using natural language processing (NLP) techniques to assess and interpret the emotions, opinions, or sentiments expressed in voters' feedback, comments, or social media posts about a candidate, issue, or election. It helps gauge public opinion by classifying sentiments into categories such as positive, negative, or neutral. This analysis can be used to predict trends, understand voter preferences, and improve political campaigns by identifying issues that resonate or concern voters.