Automatically Detecting and Rating Product Aspects from Textual Customer Reviews

This paper proposes a new approach to aspect-based sentiment analysis. The goal of our algorithm is to obtain a summary of the most positive and the most negative aspects of a specific product, given a collection of free-text customer reviews. Our approach starts by matching handcrafted dependency paths in individual sentences to find opinions expressed towards candidate aspects. Then, it clusters together di↵erent mentions of the same aspect by using a WordNet-based similarity measure. Finally, it computes a sentiment score for each aspect, which represents the overall emerging opinion of a group of customers towards a specific aspect of the product. Our approach does not require any seed word or domain-specific knowledge, as it only employs an o↵the-shelf sentiment lexicon. We discuss encouraging preliminary results in detecting and rating aspects from on-line reviews of movies and MP3 players.