The Impact of Sentiment Analysis Output on Decision Outcomes: An Empirical Evaluation

User-generated online content serves as a source of product- and service-related information that reduces the uncertainty in consumer decision making, yet the abundance of such content makes it prohibitively costly to use all relevant information. Dealing with this (big data) problem requires a consumer to decide what subset of information to focus on. Peer-generated star ratings are excellent tools for one to decide what subset of information to focus on as they indicate a review’s “tone”. However, star ratings are not available for all user-generated content and not detailed enough in other cases. Sentiment analysis, a text-analytic technique that automatically detects the polarity of text, provides sentiment scores that are comparable to, and potentially more refined than, star ratings. Despite its popularity as an active topic in analytics research, sentiment analysis outcomes have not been evaluated through rigorous user studies. We fill that gap by investigating the impact of sentiment scores on purchase decisions through a controlled experiment using 100 participants. The results suggest that, consistent with the effort-accuracy trade off and effort-minimization concepts, sentiment scores on review documents improve the efficiency (speed) of purchase decisions without significantly affecting decision effectiveness (confidence)

News Article Position Recommendation Based on The Analysis of Article's Content -Time Matters

ABSTRACT As more people prefer to read news on-line, the newspapers are focusing on personalized news presentation. In this study, we investigate the prediction of article's position based on the analysis of article's content using different text analytics methods. The evaluation is performed in 4 main scenarios using articles from different time frames. The result of the analysis shows that the article's freshness plays an important role in the prediction of a new article's position. Also, the results from this work provides insight on how to find an optimised solution to automate the process of assigning new article the right position. We believe that these insights may further be used in developing content based news recommender algorithms