Sentiment analysis is a new trend in understanding people's emotions in a variety of scenarios in their daily lives. Social media data, which includes text data as well as emoticons, emojis, and other images, would be used throughout the process, including the analysis and categorization procedures. Numerous trials were carried out in previous research using Binary and Triple Classification, however multi-class classification provides more exact and precise classification. The data would be separated into many sub-classes based on the polarity in multi-class classification. During the categorization procedure, Supervised Machine Learning Methods would be used. Sentiment levels may be tracked or studied via social media. This work examines sentiment analysis on communal media data for apprehension or detection using various artificial intelligence approaches. In the poll, it was visually campaigned that social media data, which included words, emoticons, and emojis, was used for sentiment recognition using various machine learning approaches. For sentiment analysis, the Supervised Learning with Radial Basis Function (SL-RBF) Algorithm has a greater precision value.
Sentiment Analysist, Radial Basis Function, Accuracy, Multi Class Classification, Precision
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