# Evaluating the Effectiveness of Text Pre-Processing in Sentiment Analysis
## 2 Related Work  
As the number of papers on sentiment analysis continues to increase--largely as a result of social media becoming an integral part of our everyday lives--the number of publications on text pre-processing has increased too. According to [PERSON] et al. [12], nearly 7000 papers on sentiment analysis have been indexed by the Scopus database [22] thus far. However, 99% of those papers were indexed after 2004 [12]. Over the past two decades, YouTube [23] and Facebook [24] have grown considerably. Indeed, YouTube and Facebook have reached a larger audience than any other social platform since 2019 [25]. Facebook, in particular, has held a steady dominance over the social media market throughout recent years. In the UK, for example, which is where we carried out our study, Facebook has a market share of approximately 52.40%, making it the most popular platform as of January 2021. Twitter, on the other hand, has achieved a market share of 25.45%, emerging as the second leading platform as of January 2021 [26]. Although Twitter's market share falls behind Facebook's, the amount of Twitter's publicly available data is far greater than that corresponding to Facebook. This makes Twitter remarkably attractive within the research community, and that is why we have undertaken all our work using it. Table 1 lists four of the most cited papers on text pre-processing available on Scopus. Such papers along with their references account for 279 publications, which are all represented in Figure A1 in Appendix A. Pink circles in Figure A1 represent the four papers displayed in Table 1, red circles represent the earliest publications on the subject--these are publications mostly related to the Lucene Search Engine [27]--and blue circles represent the rest of the papers. The size of the circles depends on the number of citations of the corresponding paper: the larger the circle is, the more citations the paper has. The links between the papers denote the citation relationship--paper \(A\) is linked to paper \(B\) if \(A\) cites \(B\). Figure A1 was produced with Gephi [28], an open-source network analysis and visualisation software package. The size and connectivity of the network displayed in Figure A1 in Appendix A, which is based on only four papers, shows that the body of literature on text pre-processing is large and keeps growing. However, it is still unclear which pre-processing tools should be employed, and in which order. Thus, we have evaluated various pre-processing flows quantitatively and we will present our conclusions here. A couple of influential publications that have followed a similar approach to what we aim to achieve are [PERSON] et al. [32] and [PERSON] et al. [30]. These publications are included in Table 1 and have stated the sequences of pre-processing components that they have examined and the order in which they have examined them. Table 2 displays these sequences as pre-processing flows. The first row of Table 2 indicates the datasets used by the corresponding authors to test their approaches. The remaining rows in Table 2 display the actual steps included in the pre-processing flows. As explained before, researchers have recommended the use of text pre-processing techniques before performing sentiment analysis--an example of this can be found in [PERSON] et al. [33]. However, we are not only interested in using pre-processing techniques but also in comparing different pre-processing flows and identifying the best. Pre-processing is often seen as a fundamental part of sentiment analysis [32], but it rarely is evaluated in detail. Consequently, we wanted to assess the effect of pre-processing on some off-the-shelf sentiment analysis classifiers that have become popular--namely _VADER_[34] and _TextBlob_[35]. To compare and contrast these off-the-shelf classifiers with other alternatives, we have implemented our own classifier, based on the _naive Bayes_