# Evaluating the Effectiveness of Text Pre-Processing in Sentiment Analysis
## 4 Results  
Our experiments ensured that the entire dataset was processed by each of the three classifiers described above. However, we also tested the classifiers separately with the part of the dataset for which we had a high confidence sentiment value, because we had a higher expectation that this part of the dataset would work as a _gold standard_--a collection of references against which the classifiers can be compared. Figure 8: Flow 5. All the flows were assessed by determining the sentiment using the three classifiers on both the _raw data_--that is, the dataset without any pre-processing--and the pre-processed data--that is, the dataset processed by the different combinations of the components that constitute the proposed flows. After determining the sentiment using the classifiers, we evaluated their accuracy. Table 3 shows the accuracy of the classifiers when tested with the entire dataset: first without pre-processing--raw data--and then with each of the flows. Table 3, as well as the rest of the tables in this section, shows in bold font the entry that corresponds to the highest accuracy achieved by each of the classifiers. For instance, TextBlob achieves its highest accuracy when using Flow 4; thus, the accuracy that corresponds to the combination of TextBlob and Flow 4 is shown using bold font. Table 4 shows the accuracy of the classifiers when tested with the gold standard--the part of the dataset for which we have a high confidence value. First, we tested the classifiers without any pre-processing and then with each of the pre-processing flows. According to Tables 3 and 4, there is at least one flow that increases the accuracy of each of the three classifiers evaluated, which proves the importance and benefits of pre-processing. From Tables 3 and 4, we can confirm that Flow 4 offers the greatest advantages for two of the three classifiers. TextBlob achieves its greatest accuracy with Flow 4 when tested with both the entire dataset and the gold standard. Similarly, naive Bayes achieves its greatest accuracy with Flow 4 when tested with the entire dataset and the gold standard. Recall that Flow 4 includes all the pre-processing components listed in Section 3.2, except for spelling correction and lemmatisation. Given that naive Bayes performed better than the other two classifiers in all cases, regardless of the pre-processing flow chosen, we opted to evaluate this classifier further. We started by testing naive Bayes exclusively with negative tweets, then exclusively with positive tweets, and finally with all the tweets. The results of this additional evaluation are presented in Table 5. Note that the results of testing exclusively with negative tweets are shown in the row titled \"Naive-Bayes Negative\", the results of testing exclusively with positive tweets are shown in the row titled \"Naive-Bayes Positive\", and the results of testing with all the tweets are shown on the row titled \"Naive-Bayes Total\". We also evaluated naive Bayes when tested, separately, with the positive and negative tweets included in the gold standard. The results are presented in Table 6. It should be observed that Flow 4 allows the naive Bayes classifier to achieve its highest accuracy with the entire dataset, when tested exclusively with positive tweets and with all the tweets together--see Table 5. Additionally, Flow 4 allows the naive Bayes classifier to achieve the highest accuracy with the gold standard, when tested exclusively with positive tweets and with all the tweets together--see Table 6. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Raw Data** & **Flow 1** & **Flow 2** & **Flow 3** & **Flow 4** & **Flow 5** \\ \hline TextBlob & 73.43 & 71.42 & 71.40 & 73.78 & **73.93** & 73.30 \\ \hline VADER & 74.44 & 72.35 & 71.90 & 73.93 & 74.10 & **74.68** \\ \hline Naïve Bayes & 88.50 & 88.28 & 88.42 & 88.98 & **91.72** & 89.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy of the classifiers on the gold standard.