## 4 Performance Evaluation
### Experimental Results  
Three groups of cross-validated experiments are carried out to compare the proposed scheme with the existing ones. As a workload of Sentiment 140 is built from 800,000 negative and 800,000 positive documents of labeled tweets, the data is reconstructed as pairs of one negative and one positive tweet. For each experiment, 10,000 distinct random numbers (half odd and half even) are generated using the MATLAB function, ranging from one to 1,600,000. A training dataset is then constructed by extracting the tweets of the corresponding index from Sentiment 140, which is composed of 5000 negative and 5000 positive randomly selected documents. The tested dataset is selected from the remaining 1,500,000 documents of tweets. The first group of experiments tries to investigate the performances in processing the tested data with balanced class distribution. Here, the tested datasets ranging from 500 to 6000 elements are built, consisting of equal number of positive and negative randomly selected documents. Secondly, two experiments are implemented to compare the performances of the schemes in handling biased tested data, consisting of solely positive or negative instances. Finally, they are evaluated with the tested documents of random polarity. Figure 4 shows the performance of classification of the five schemes when the size of the balanced tested dataset varies from 500 to 6000. Observe from the figures that the proposed CDS-C scheme consistently outperforms the other schemes for all four metrics regardless of the size of the tested data. Evaluating the tweets that have negative and positive polarity of the same size is common. The proposed scheme presents the highest accuracy, precision, recall, and F1-score, which indicates that it is very sensitive to the orientation of sentiment, and is able to classify the tested data into the correct category. This is achieved by the weights that were computed in accordance with both inter-category and intra-category feature relevance, and the discriminative features. Moreover, the proposed models of CDS and IDM properly measure the discriminability of the features of the training dataset, which contributes to quantify the usefulness of the features. In general, the classification performance tends to degrade when the number of the tested data increases. This is because a training corpus of limited size cannot sufficiently cover the increased size of the tested data. As depicted in Figure 4a, the accuracy slightly decreases as the number of tested documents increases from 500 to 6000. Also, it can be seen from Figure 4a,d that the accuracy and F1-score of the DF are superior than TF because considering either the presence or absence of features allows higher classification performance than considering only their frequencies [56]. Figure 3: Two examples of tweets in the dataset. Table 3 shows two tweets extracted from Sentiment 140, and Table 4 shows a comparison of the polarity label assigned with the five approaches evaluating the tested tweets of Table 3. Observe from Table 3 that the indicator is the predefined polarity label as \"0\" for negative and \"4\" for positive. As Table 4 shows, _P(neg1 test)_ and _P(pos1 test)_ are the final sentiment values generated by the MNB classifier of the tested tweets in the negative and positive categories, respectively. The final polarity label is obtained by comparing the two values.