Study of Sentiment Classification Techniques - Sharma, S; Singh, D (International Journal of Computer Sciences and Engineering, 2025)
Sentiment analysis is important part of text mining. From the last few years social networking sites like Facebook, Twitter, and Amazon generates large amount of data, such vast amount of data contains lots of useful information which need to be analyzed. As we all know social networking sites generate data in huge amount not only in terabyte but in petabyte so processing of such large amount of data is big challenge, sentiment analysis is the technique which help to analyzie such raw amount of data and extract useful information from it. The reason behind using Sentiment analysis is that it analyze such large amount of data and extract useful information from it that it analyze such large amount of data and extract useful inform Sentiment analysis helps business and organization because it’s easy for them to know how people feel about their product or services so that they can make better decision or improve their services. For that purpose we have different sentiment analysis techniques like Naïve bayes, Maximum Entropy, SVM which gives correctness of information or provides us accuracy. For sentiment we use machine learning because it train the computer to recognize the emoticon behind the sentence.