Dissertation presented as partial requirement for obtaining the Master`s degree in Information Management, with specialization in Marketing IntelligenceConsumers trust on online reviews to help them making their purchasing decisions. Online reviews provide consumers with clues about the quality of the products that they want to buy. Consumers rely on clues, such as, review helpfulness votes and rating to infer product quality. In this study, we perform a Text Clustering and a Text Categorization analysis to uncover the review characteristics and to predict the review rating, helpfulness votes and the product price, based on review corpus. We use a dataset with 72 878 reviews of unlocked mobile phones sold on Amazon.com to perform this analysis. The main goal of this research is to understand the impact of review valence, rating and type on helpfulness votes on Amazon, for unlocked mobile phones. This research aims, also to understand the impact of price on customer satisfaction and the relationship between customer satisfaction and ratings.
Our results suggest that positive reviews that emphasize the feature level quality of the products receive more helpful votes than the positive reviews that contain mainly subjective expressions or negative reviews. Another important finding of this research is on the influence of the price of the product. The phones with high price tend to receive more positive reviews and more helpful votes. These findings have important managerial and theoretical implications. To best of our knowledge, our study is the first one to analyze the effect of the combination of valence, rating and subjectivity of the review text on helpful votes
