| dc.description.abstract | Past few decades have seen a widespread use and popularity of online review sites such as Yelp, TripAdvisor, etc. As many users depend upon reviews before deciding upon a product, businesses of all types are motivated to possess an expansive arsenal of user feedback (preferably positive) in order to mark their reputation and presence in the Web (e.g., Amazon customer reviews). In spite of the fact that a huge extent of buying choices today are driven by numeric scores (e.g., movie rating in IMDB), detailed reviews play an important role for activities like purchasing an expensive mobile phone, DSLR camera, etc. Since writing a detailed review for an item is usually time-consuming and offers no incentive, the number of reviews available in the Web is far from many. Moreover, the available corpus of text contains spam, misleading content, typographical and grammatical errors, etc., which further shrink the text corpus available to make informed decisions.
In this thesis, we build an novice system AD-WIRE which simplifies the user`s task of composing a review for an online item. Given an item, the system provides a top-k meaningful phrases/tags which the user can connect with and provide reviews easily. Our system works on three measures relevance, coverage and polarity, which together form a general-constrained optimization problem. AD-WIRE also visualizes the dependency of tags to different aspects of an item, so that user can make an informed decision quickly. The current system is built to explore review writing process for mobile phones. The dataset is crawled from GSMAreana.com and Amazon.com. | |
