Evaluating the influence of feature selection-based dimensionality reduction on sentiment analysis

Abstract

As social media has become an integral part of digital medium, the usage of the same has increased multi-fold in recent years. With increase in usage, the sentiment analysis of such data has emerged as one of the most sought research domains. At the same time, social media texts are known to pose variety of challenges during the analysis, thus making pre-processing one of the important steps. The aim of this work is to perform sentiment analysis on social media text, while handling the noise effectively in the data. This study is performed on a multi-class twitter sentiment dataset. Firstly, we apply several text cleaning techniques in order to eliminate noise and redundancy in the data. In addition, we examine the influence of regularized locality preserving indexing (RLPI) technique combined with the well-known word weighting methods. The findings obtained from experiment indicate that, RLPI outperforms other algorithms in feature selection and when paired with long short-term memory (LSTM), the combination outperforms other classification models that are discussed.