Sentence examples for review datasets from from inspiring English sources

For each feature selection method, we tried six feature sizes at 500, 1000, 1500, 2000, 2500, and 3000, since this is the range typically considered for text classification, and in terms of total features, we have 9000 18,000 for the Turkish review datasets, and 8000 38,000 for English review datasets from our baseline systems.

Feature sizes are selected in the range from 500 to 3000 with 500 increments, compared with the total feature sizes ranging from 8000 to 18,000 for the Turkish review datasets and from 8000 to 38,000 for English review datasets.

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.

We use Turkish and English review datasets in our experiments.

We have grouped collected review datasets into reviews by the recommended review group (non-spam) and reviews by the fake review group (filtered).

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

Bai [25] improved the accuracies from baseline 84.1 92.7% using their proposed Tabu search-enhanced Markov blanket model for the movie review dataset.

The authors tested their models using the Amazon Mechanical Turk (AMT) synthetic fake reviews dataset on a real-world fake reviews dataset procured from Yelp.

They assembled two complementary datasets from Yelp and provided empirical support for using filtered reviews as a proxy for review frauds [27].

The statistical challenges and some of the key concepts in analyzing dense datasets from high-throughput assays are briefly reviewed.

We aim to include datasets from all studies meeting the inclusion criteria of the original (updated) reviews.