Word embedding-based approach to aspect detection for aspect-based summarization of persian customer reviews
Seyyed Aref Razavi, Masoud Asadpour · 2017
Many1 ecommerce websites provide the customers with the ability to share their opinions about the products. These opinions can assist other customers to purchase wisely and manufacturers to improve their products and services. Due to a huge volume of product reviews in the online websites and hence difficulty of perusing all of them, it is essential to produce a concise summary of the reviews about different aspects of the products. Aspect detection is a vital step of aspect-based summarization, aiming at identifying the most important product aspects about which users express their opinions. In this paper, we propose a novel unsupervised approach to aspect detection employing word embedding techniques to identify relevant aspects and their semantically related words, called aspect keywords and categorize aspects into semantic categories. The main purpose of our method is to use semantic and syntactic relationships in word embedding vectors in order to improve extraction of multiword aspects and distinguishing explicit and implicit aspects from their keywords. Our experimental results indicate the improvements.