Estimating the Rating of Reviewers Based on the Text
Table 6: Result of the test set
['SVM (Overall Accuracy) Info-Gain-Top 200', 'SVM (Overall Accuracy) 78', 'NB (Overall Accuracy) 72']
(83 percent for SVM) , we showed that the result of this research is almost comparable with the other works in this area. One important note here is that reviewed works leveraged various types of features as well sentiment. The result presented in this work is solely based on lexical features. To take a step further, we tested the trained classifier on the 10 percent test set data (as explained before). The results are slightly different from what we achieved earlier. Since we have a small data set, we decided to choose (1) top 200 words to avoid overfitting, (2) and top 600 words as one of the best features. Same as the training, SVM resulted in higher accuracy compared to NB. However, the 200 info-Gain words performed better.