The problem of data classification is an important topic in the field of machine learning and information retrieval. This has been widely studied and has been applied in many fields. There are multiple models which are proposed for the classification, like tree-structured classifiers, genetic algorithms, Bayesian classification, neural networks etc. These have a large range of applications in different areas like, fraud/spam detection, Customer Segmentation, Medical Diagnosis, Credit approval, weather prediction etc. This project tries to aim at a particular subclass of classification, namely sentiment analysis. Hybrid techniques should be applied in this field of study as each of the existing models have brought about some new expertise and their improvements need to be combined to give higher performance and accuracy. The sentiment analysis problem requires to take as input a block of text and correctly predict the sentiment of the writer or the speaker of the text. We have sufficient data to build a system that uses hybrid techniques like Naïve Bayes and combines the existing models to perform sentiment analysis on a dataset and study its results. The hybrid approach using Naïve Bayes to this problem gives promising results.
A hybrid approach to the sentiment analysis problem at the sentence level
