Sentence examples for review datasets from inspiring English sources

We use Turkish and English review datasets in our experiments.

DFD does reasonably well across all review datasets, but it tends to favour bigger feature sizes.

Our results show that for all Turkish review datasets, the best results are all obtained with the NBM classifier, and for some English review datasets, LR and SVM have the best performance.

As can be seen in Table 1, the total number of features without any reduction ranges from 9000 to 18,000 for the Turkish review datasets, and 8,000 38,000 for the English review datasets.

Also compared with the Turkish review datasets, DFD is not as good as CHI2 and IG for the English review datasets, even though the performance is close for the kitchen reviews and generally better than OCFS.

For differences, the English review datasets usually have bigger vocabulary, resulting in relatively bigger feature sizes for feature selection.

Their method achieves an accuracy of 92.7% for the movie review dataset.

Also, the best results for each review dataset are given in bold-face.

Fig. 1 Detailed results of feature sizes for the Turkish electronic review dataset.

As observed in Table 2, our new method QER is the best performer for each review dataset.

The Turkish product review dataset is collected from an e-commerce website (http://www.hepsiburada.com) from different domains [28].