With the help of Web-2.0, the Internet offers a vast amount of reviews on many topics and in different domains. This has led to an explosive growth of product reviews and customer feedback, which presents the problem of how to handle the abundant volume of data. It is an expensive and time-consuming task to analyze this huge content of opinions. Therefore, the need for automated sentiment analy...

With the surge in the number of the online review, how to get valuable information from a large number of useless information become a new problem that people face with when they shopping online. So far, many scholars have carried on some researches on this problem, but most of the studies were based on tangible goods' online reviews, and studies about services' online reviews are still very fe...

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidencesconclusions ratios, are good indicators of helpful reviews. To validate this hypothesis,...

Technological advances in the digital space have provided renewed impetus to businesses. Costly, labor-intensive marketing campaigns been replaced by marketing. However, along with benefits, increasing sophistication and exponential growth of e-commerce businesses also introduced new challenges. The large number similar product offerings high volume reviews created a technology-induced hurdle f...

We present an algorithm for automatically ranking usergenerated book reviews according to review helpfulness. Given a collection of reviews, our REVRANK algorithm identifies a lexicon of dominant terms that constitutes the core of a virtual optimal review. This lexicon defines a feature vector representation. Reviews are then converted to this representation and ranked according to their distan...

We investigate a food review dataset from Amazon with more than 500000 instances, and utilize information from the data set such as the text review, score, helpfulness, etc. Instead of the traditional word representation using frequency, we use skip-gram to train our own word vectors using the pretrained GloVe Twitter word vector as the initialized value. We also use recursive parsing tree to t...

Online reviews provided by consumers are a valuable asset for e-Commerce platforms, influencing potential consumers in making purchasing decisions. However, these reviews are of varying quality, with the useful ones buried deep within a heap of non-informative reviews. In this work, we attempt to automatically identify review quality in terms of its helpfulness to the end consumers. In contrast...