Polarity of Ionic Liquids

The Movie reviews and ratings are used by the people to decide which movie to buy or watch. Movie reviews are used as a recommendation, on whether it's worth spending time and money to watch or buy a movie. Reviews contain both positive and negative opinion on the movie. Ratings are calculated based on the total positive reviews of the movie. Therefore it is important to identify whether the given review is a positive or negative. In this paper we have used movie review dataset (aclimdb) to train our machine and logistic regression algorithm which is used to predict the polarity of a given movie review. Introduction Decision making place an important role in human life, a good and correct decision will make our life better. Decisions can made based on others opinions. Manual efforts on thinking and wasting time in taking decisions are decreased. In case of deciding on which movie to buy or watch, people look at websites, which provides reviews and ratings of the movies. This makes people to decide on which movie to buy or watch. People may have good or bad opinion about the movie, they express there opinion through reviews in the websites like amazon, BookMyShow etc. Where people look at the reviews and ratings to decide which movie to watch or buy. Good opinions about the movies are classified as positive and bad opinions are classified as negative. Review containing "good", "wonderful", "enjoyed" like keywords are called as positive. Review containing "not nice", "bad" like keywords are called as negative. Movie ratings are calculated based on the positive opinion about the movie. Sentiment analysis is a technique used to classify positive and negative opinion of the movie review. Where sentiment analysis is text classification tool, which analyses the text and identifies the polarity of the text.