Sentiment Analysis Using Tuned Ensemble Machine Learning Approach

Abstract

With the recent emergence of Web-based applications and use of social networking sites, number of people are eager in expressing their views and opinions online. The sentimental analysis also referred to as opinion mining aims at processing user reviews (about products, movies, services, books, places, etc.). These reviews are often unstructured and need processing to evolve into the productive knowledge. Majority of the sentiment analysis works on the classification of opinion polarity with the use of simple classifiers. Handling diverse data distribution is one of the major issues that simple classifiers suffer. To cope up with the issue in this paper, we utilized the ensemble learners on the polarity prediction of the movie reviews. The proposed work processes the review data through some elementary steps that are conducted for the feature extraction in sentiment analysis. In addition to the feature extraction, we further perform the feature selection for the sake of dimensionality reduction. However, in contrast to the conventional simple learner, we applied the ensemble learner in the proposed model and evaluated its performance. To compare the ensemble model competence, we conducted the experiment on both individual as well as ensemble learner (random forest, AdaBoost, extra trees) and computed classification measures on both the model. IMDB dataset is used, and the polarity of a review, i.e., whether it is positive or negative, is predicted. With an extensive experimentation, it is found that results of ensemble classifiers are outperforming than individual learner in the classification of sentiment polarity.