Sentiment Analysis on Movie Reviews

Abstract

Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic from a structured, semi-structured or unstructured textual data. we try to focus our task of sentiment analysis on IMDB movie review database. Sentiment Analysis is a process of extracting information from large amount of data, and classifies them into different classes called sentiments. Python is simple yet powerful, high-level, interpreted and dynamic programming language, which is well known for its functionality of processing natural language data by using NLTK (Natural Language Toolkit). NLTK is a library of python, which provides a base for building programs and classification of data. NLTK also provide graphical demonstration for representing various results or trends and it also provide sample data to train and test various classifier respectively. Sentiment classification aims to automatically predict sentiment polarity of users publishing sentiment data. Traditional classification algorithm can be used to train sentiment classifiers from manually labeled text data . We directly apply a classifier trained to the domain to the performance will be very low due to the difference between these domains. In this work, we develop a general solution to sentiment classification when we do not have any labels in target domain but have some labeled data in a different domain, regarded as source domain .In this project, we attempt not to only detect sarcasm in text and made pilot Model Sarcasm with Naive Bayes using TFIDF feature vectors.