The effect of preprocessing techniques on Twitter sentiment analysis

Abstract

As Twitter offers a fertile ground for expressing different thoughts and opinions, it can be seen as a valuable tool for sentiment analysis. Furthermore, properly identified reviews present a baseline of information as an input to different systems, such as e-learning systems, decision support systems etc. However, the data preprocessing is a crucial step in sentiment analysis, since selecting the appropriate preprocessing methods, the correctly classified instances can be increased. In view of the above, this research paper explains the necessary information to get preprocess the reviews in order to find sentiment and make analysis whether it is positive or negative. Extended comparison of sentiment polarity classification methods for Twitter text and the role of text preprocessing in sentiment analysis are discussed in depth. In the set of tests, possible combinations of methods and report on their efficiency were included, conducting experiments using manually annotated Twitter datasets. Finally, it is proved that feature selection and representation can affect the classification performance positively.