I am currently doing sentiment analysis using Python. Here I am taking all the reviews from movie dataset and using Naive Bayes algorithm to predict whether the review is positive or negative. From the input dataset, I am using a logic to remove stopwords and after that training my dataset to predict the result. As of now, my test results are predicted, but few results are predicted wrongly.
This was a great film. Jack Halley is comical as usual and Bela Lugos is hilarious even in his tiny role. I love this flick.
Example, the above sentence is positive, but it is predicting as negative. Similar instance occurs to many other scenarios. If it has some sarcasm, then there are chances that it may predict the other way. But for few direct reviews, it is predicting the other way. Could you suggest other ways to better my algorithm, so that it increases the accuracy of prediction. Any other efficient methods apart from removal of stopwords? I have been looking for various suggestions through internet but I am new to this topic and hence couldn't find an effective way to proceed. Any good suggestions would be greatly appreciated.