Feature Sentiment Diversification of User Generated Reviews: The FREuD Approach
published: April 3, 2014, recorded: July 2013, views: 2143
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Online discussions, user reviews and comments onthe Social Web are valuable sources of informationabout products, services, or shared contents. The rapidlygrowing popularity and activity of Web communitiesraises novel questions of appropriate aggregation anddiversification of such social contents. In many cases,users are interested in gaining an extensive overviewover pros and cons of a particular track of contributions.We address the problem of social content diversificationby combining latent semantic analysis with featurecentricsentiment analysis. Our FREuD approach providesa representative overview of sub-topics and aspectsof discussions, characteristic user sentiments underdifferent aspects, and reasons expressed by differentopponents. In experiments with real world productreviews we compare FREuD to the typical implementationof ranking reviews by the usefulness rating providedby users as well as a naive sentiment diversificationalgorithms based on star ratings. To this end we hadhuman users provide a fine-grained gold standard aboutthe coverage of features and sentiments in reviews forseveral products in three categories. We observed thatFREuD clearly outperforms the baseline algorithms ingenerating a sentiment-diversified set of user reviewsfor a given product.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !