Textual factors in online product reviews: a foundation for a more influential approach to opinion mining

While data mining is well established in practice, opinion mining is still in its infancy, with issues in particular around the development of methodologies which effectively extract accurate, reliable, influential and useful information from the raw opinion data collected from informal product reviews. Current approaches adopt a single-variable approach, focusing on individual metrics-word length, the presence of keywords, or the overall semantic orientation of terms within the data-while neglecting to evaluate whether these individual artifacts are indicative of the tone of a given review. This approach has significant limitations when we move from trying to merely evaluate whether an online opinion is positive or negative, to trying to evaluate how likely it is that the opinion will influence others. Given this issue, one promising avenue would be to evaluate the general analysis approaches utilized by opinion mining algorithms and identified in the literature in terms of how accurately they reflect how people actually interpret and are influenced by electronic online reviews. Through interviewing and a follow up survey of 136 participants, the validity of the approach in terms of ascertaining the tone of a piece of text can be evaluated, as well as the identification of measurable factors within text which make a given opinionated text more or less influential in an online context, further facilitating the development of more effective multivariate opinion mining approaches. Furthermore, the identification of factors which make an online opinion text more or less persuasive helps to facilitate the development of opinion mining approaches which can evaluate how likely a review is to affect an individual's decision making.