Feature word selection by iterative top-K aggregation for classifying recommended shops

摘要

We propose a feature word selection method for classifying recommended shops using Yelp customer reviews. TextRank keywords are extracted from the customer reviews to construct the sorted positive and negative keyword lists based on each keyword's summed TextRank scores. The top-K keywords are then aggregated iteratively by multiples of K to construct the positive and negative keyword frequency lists. The negative keyword frequency list is then subtracted from the positive keyword frequency list, and the resulting list is standardized to generate the final positive and negative keyword lists. The performance of our feature selection method is evaluated using Naïve Bayes classifiers, and the binary classification accuracy of the selected feature words is 77.94%, which is better than the baseline χ2 feature word selection.