Context Extraction from Reviews for Context Aware Recommendation Using Text Classification Techniques

In this paper, we investigate the use of Text Classification techniques to extract contextual information from user reviews for Context Aware Recommendation. We conduct several experiments to identify the best Text Representation settings and the best classification algorithm for our dataset. We carry out our experiments on hotel reviews. We focus on extracting the trip type, as contextual information, from these reviews. Results show that the Naive Bayes classifier yields the best results with up to 72.2% in terms of F1-measure. To extract context from user reviews with text classification techniques, we recommend to use raw text rather than employing stemming, to use the normalized frequency based weighting rather than the presence based one, to remove terms that occur once in the data set, and to combine unigrams, bigrams and trigrams.