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Sentiment Analysis using Machine Learning

Twitter is one of the broadly used social networking structures with greater than 192 million each day lively customers and a 500 million Tweets. Basically, Twitter is reaching a large target audience, connects quite a few users and also gives a useful factor of view on an ongoing difficulty, based on their interest. This paper consists of sentiment evaluation on twitter statistics that is related to Airline critiques using Machine Learning, NLP. Basically customers and their rankings on the airlines they would choose for their journey are evaluated. Positive, negative and neutral opinions of the users are estimated based on their tweets in the twitter. Data visualization which may include cleaning, checking and transforming the pattern is also used. Natural Language Processing (NLP) and Machine learning are used on this data-driven model for prediction. Machine learning techniques are applied to the corresponding twitter dataset containing reviews of Airlines to find out the necessary information and inference. These algorithms are used to extract the sentiments of the user and to discover future trends based on current usage.