Abstract
For the last two decades, various studies on determining the quality of online product reviews have been concerned with the classification of complete documents into helpful or unhelpful classes using supervised learning methods. As in any supervised machine-learning task, a manually annotated corpus is required to train a model. Corpora annotated for helpful product reviews are an important resource for the understanding of what makes online product reviews helpful and of how to rank them according to their quality. However, most corpora for helpfulness are annotated on the document level: the full review. Little attention has been paid to carrying out a deeper analysis of helpful comments in reviews. In this article, a new annotation scheme is proposed to identify helpful sentences from each product review in the dataset. The annotation scheme, guidelines and the inter-annotator agreement scores are presented and discussed. A high level of inter-annotator agreement is obtained, indicating that the annotated corpus is suitable to support subsequent research.
Highlights
Opinions play a significant factor in people's decision-making
The kappa results we obtained for Inter-annotator agreement (IAA) were interpreted based on the classification of Landis and Koch (1977) which is shown in table 3
Previous work in the field of corpus construction for sentiment analysis of product reviews has mainly been concerned with the manual annotation of positive or negative orientation towards a product
Summary
Opinions play a significant factor in people's decision-making. Individuals are influenced by others' advice and evaluations in the process of taking a decision. Word-of-mouth communication is a well-known means to shape consumers’ attitudes towards a product (Brown & Reingen, 1987). According to Harrison-Walker (2001), the phrase word-of-mouth refers to: “informal, person-to-person communication between a perceived noncommercial communicator and a receiver regarding a brand, a product, an organization, or a service”. The Internet makes it possible for individuals to read about experiences of other individuals, through what is called electronic word-ofmouth (eWOM). Since the emergence of Web 2.0, there has been an explosive growth of eWOM, called usergenerated content (UGC), such as forums, product reviews and web blogs
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