Sentiment categorization has been used in a variety of contexts, including reviews of movies and products as well as analyses of customer feedback. The foundation of sentiment analysis is the ability to label a piece of text as positive or negative. Emotions are hard to categorise since the meanings of words and phrases shift with context. As innovative business intelligence options, data mining and machine learning may facilitate the real-time processing of massive volumes of internet data. Another new innovation in online text mining is sentiment analysis, which measures the positive or negative emotional tone of written content. Sentiment analysis is a method for determining an individual's attitude towards a subject. Finding of the study shows that Text Mining Methodology for Business Intelligence is discussed in this chapter, which covers one of the submodules of the hybrid approach (TMABI). In this chapter, the emphasis was placed on the categorization of sentiments using a modified version of the LSA methodology, which, in comparison to other methods already in use, produces more effective results. In the next chapter, the whole operation of the hybrid system, which is an innovative method for gathering business information using text mining, will be presented.

Modified Latent Content, Sentiment Analysis, Machine learning, etc.