Contextual and Position-Aware Factorization Machines for Sentiment Classification
Table 3: Data Information
['[BOLD] Data  [BOLD] Dataset', '[BOLD] Data  [BOLD] Source', '[BOLD] Training (docs)  [BOLD] Average Length', '[BOLD] Testing (snippets)  [BOLD] Average Length']
We use three real-word review datasets. The label for a full review can be directly obtained because a rating score is often provided, but the label for a text snippet requires human labeling. They are word phrases or short sentences (snippets from full reviews) about movie from IMDB, cellphone from Amazon and restaurant from Yelp. Each set contains 1000 snippets (500 positive/negative). For full reviews, we use the movie review dataset from Pang and Lee For consistency, 1000 positive and 1000 negative reviews are sampled from cellphone and restaurant. Reviews with rating scores 5 and 4 are treated as positive and scores 2 and 1 are treated as negative like Chen et al. For the first task, the document-level sentiment classification, we conduct 5-folds cross validation using only full reviews. We split the data to 70%, 10%, 20% for training, validation, and testing for each data set. For the second task, we use the classifiers trained by all full reviews to classify review snippets. Notice that we have kept the same parameter settings for the classifiers used in both tasks.