Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

Feature selection and sentiment analysis are two common studies that are currently being conducted; consistent with the advancements in computing and growing the use of social media. High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features. Furthermore, most reviews from social media carry a lot of noise and irrelevant information. Therefore, this study proposes a new text-feature selection method that uses a combination of rough set theory (RST) and teaching-learning based optimization (TLBO), which is known as RSTLBO. The framework to develop the proposed RSTLBO includes numerous stages: (1) acquiring the standard datasets (user reviews of six major U.S. airlines) which are used to validate search result feature selection methods, (2) pre-processing of the dataset using text processing methods. This involves applying text processing methods from natural language processing techniques, combined with linguistic processing techniques to produce high classification results, (3) employing the RSTLBO method, and (4) using the selected features from the previous process for sentiment classification using the Support Vector Machine (SVM) technique. Results show an improvement in sentiment analysis when combining natural language processing with linguistic processing for text processing. More importantly, the proposed RSTLBO feature selection algorithm is able to produce an improved sentiment analysis.