Are influential writers more objective?: an analysis of emotionality in review comments

People increasingly rely on other consumers' opinion to make online purchase decisions. Amazon alone provides access to millions of reviews, risking to cause information overload to an average user. Recent research has thus aimed at understanding and identifying reviews that are considered helpful. Most of such works analyzed the structure and connectivity of social networks to identify influential users. We believe that insight about influence can be gained from analyzing the affective content of the text as well as affect intensity. We employ text mining to extract the emotionality of 68,049 hotel reviews in order to investigate how those influencers behave, especially their choice of words. We analyze whether texts with words and phrases indicative of a writer's emotions, moods, and attitudes are more likely to trigger a genuine interest compared to more neutral texts. Our initial hypothesis was that influential writers are more likely to refrain themselves from expressing their sentiments in order to achieve a more perceived objectivity. But contrary to this initial assumption, our study shows that they use more affective words, both in terms of emotion variety and intensity. This work describes the first step towards building a helpfulness prediction algorithm using emotion lexicons.