Kumar Abhishek, Mayank Mehiral, M. S. Sathvik Murthy


Sentimental analysis is defined as the use of computational linguistics, natural language, text analysis and biometrics to recognise,extract,test and study useful attributes and their information. We considered two different datasets both pre-dominantly pertaining to IMDB as source. One of the considered datasets composed only textual content which was processed by removing unnecessary contents and distributed into two categories namely positive and negative. We further divided the data into training dataset and testing dataset. Using more relevant training algorithms such as logistic regression and decision tree algorithm, we had more relevant attribute which helped us in training our model to predict if a review is positive or negative.

 When this analysis is linked with other attributes of any product of interest, we can accurately pin point or predict a product’s rating even before it sees broad day light. 



Keywords - Sentimental Analysis, Tokenization, Data pruning, Accuracy, Polarity.

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