Evaluating the Effectiveness of Text Pre-Processing in Sentiment Analysis

Practical demands and academic challenges have both contributed to making sentiment analysis a thriving area of research. Given that a great deal of sentiment analysis work is performed on social media communications, where text frequently ignores the rules of grammar and spelling, pre-processing techniques are required to clean the data. Pre-processing is also required to normalise the text before undertaking the analysis, as social media is inundated with abbreviations, emoticons, emojis, truncated sentences, and slang. While pre-processing has been widely discussed in the literature, and it is considered indispensable, recommendations for best practice have not been conclusive. Thus, we have reviewed the available research on the subject and evaluated various combinations of pre-processing components quantitatively. We have focused on the case of Twitter sentiment analysis, as Twitter has proved to be an important source of publicly accessible data. We have also assessed the effectiveness of different combinations of pre-processing components for the overall accuracy of a couple of off-the-shelf tools and one algorithm implemented by us. Our results confirm that the order of the pre-processing components matters and significantly improves the performance of naïve Bayes classifiers. We also confirm that lemmatisation is useful for enhancing the performance of an index, but it does not notably improve the quality of sentiment analysis.