Impact of Text Pre-Processing on the Performance of Sentiment Analysis Models for Social Media Data
Valentine Velaphi Nhlabano, Patricia E.N. Lutu · 2018
Sentiment analysis also known as opinion mining is the study of people's opinions, attitudes, appraisals and emotions regarding events, individuals, issues, entities, topics and their attributes. The concept of Sentiment Analysis can also be extended to include detecting emotional status such as sadness, anger, and happiness. The purpose of Sentiment Analysis is to predict the polarity of a given text item in order to determine if the author is expressing positive, negative or a neutral opinion about a given topic. Sentiment Analysis has become an interesting research topic over the years due to a wide variety of its practical applications, its promising commercial benefits and also due to the many interesting challenges and research problems it presents to the research field. In Sentiment analysis one of the most crucial step is text pre-processing which is treated as a challenging text classification task as it classifies the orientation of a text into either positive or negative. In this study we investigates the influence of Text pre-processing methods used for Social media data. The experimental results demonstrate that Text pre-processing methods increases the predictive accuracy of the resulting models for sentiment classification.