My objective is to classify the sentiment of a tweet/text by applying lexical and supervised machine learning techniques by cleaning and preparing data with the help of natural language processing. A comparative study of two algorithms ­ Naive Bayes and Support Vector Machines is performed. I achieve feature vectors or feature extraction from the tweet, required for the classifiers, with the help of hand­coded rules and language processing. The core of my capstone project lies in detecting the person’s mood or emotion based on the positivity or negativity of the tweet. I plan to achieve my goal with the help of different techniques for processing a tweet, which is in the form of a natural english written language. The field of sentiment analysis is very useful for determining the opinions of different people on various topics mentioned above as they post on social media websites. This area helps an analyst to classify the popularity or decline of a certain trending news or based on the input hash tag provided by an end­user for a sentiment analysis­based application.


Title Updated At Action
Report 2019-10-28 08:30:26
Poster 2019-10-28 08:30:26
Title Updated At Action


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