Sentiment Analysis of tweets using bag of words

Internet based life investigation is the way toward gathering information from prevalent person to person communication benefits and anticipating the general visibility on some random area dependent on the examination of the gathered information. This is accomplished utilizing AI and normal language handling strategies, alongside different python libraries, for example, matplotlib, tweepy and textblob. The slant examination device has a basic UI, which requests a catchphrase dependent on which investigations of the tweets containing the watchword are isolated and measurably spoke to as far as the suppositions being communicated as positive, negative or nonpartisan. The initial move towards preparing the AI model starts with gathering the datasets that are made accessible utilizing the Twitter API, which is accomplished utilizing the Tweepy library. It offers named datasets that can be utilized to effectively prepare the AI model. The tweet handling and arrangement is finished utilizing the textblob library, which offers a straightforward API for characteristic language preparing undertakings, for example, assessment investigation and order. With this content classifier, we can mark each Tweet as positive, negative or unbiased of the wistful incentive in no time flat. Be that as it may, human language is perplexing. Showing a machine to dissect the different linguistic subtleties alongside assorted social varieties, slang and incorrect spellings that happen in web based life make the procedure complex and showing a machine to see how the setting influences the tone demonstrates to be very testing. Assessment investigation has its own restrictions like some other ML prescient and isnt utilized as a 100% precise marker however with a little supervision it very well may be an incredible resource. Keywords:Sentiment analysis, Tweepy, TextBlob, Twitter.