We apply Supervised Machine learning classifiers to text obtained from Facebook to categorize them as Positive, Negative, Neutral. The classifiers used are Mulitnomial Naive Bayes, Support Vector machine, K Nearest Neighbours. They are trained using Sentiment140 Dataset of 1600000 tweets. Python is used as the programming language along with Packages NLTK(Natural Language Toolkit, SciKit Learn, Matplotlib, Pandas).
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Applies a machine learning pipeline to data obtained from Facebook. Feature extraction, pre-processing, feature selection, training, testing different classifiers and comparing their accuracy.
himanshu-14/facebookopinion
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Applies a machine learning pipeline to data obtained from Facebook. Feature extraction, pre-processing, feature selection, training, testing different classifiers and comparing their accuracy.
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