Predictive aspect-based sentiment classification of online tourist reviews

With the increase of online tourists reviews, discovering sentimental idea regarding a tourist place through the posted reviews is becoming a challenging task. The presence of various aspects discussed in user reviews makes it even harder to accurately extract and classify the sentiments. Aspect-based sentiment analysis aims to extract and classify user's positive or negative orientation towards each aspect. Although several aspect-based sentiment classification methods have been proposed in the past, limited work has been targeted towards the automatic extraction of implicit, infrequent and co-referential aspects. Moreover, existing methods lack the ability to accurately classify the overall polarity of multi-aspect sentiments. This study aims to develop a predictive framework for aspect-based extraction and classification. The proposed framework utilises the semantic relations among review phrases to extract implicit and infrequent aspects for accurate sentiment predictions. Experiments have been performed using real-world data sets crawled from predominant tourist websites such as TripAdvisor and OpenTable. Experimental results and comparison with previously reported findings prove that the predictive framework not only extracts the aspects effectively but also improves the prediction accuracy of aspects.