Sentiment analysis of user comments for one-class collaborative filtering over ted talks
Published 28 July 2013Open access
Nikolaos Pappas, Andréi Popescu-Belis
Citations78
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A sentiment-aware nearest neighbor model (SANN) for multimedia recommendations over TED talks, which makes use of user comments is proposed, which outperforms significantly, by more than 25% on unseen data, several competitive baselines.
Abstract
User-generated texts such as reviews, comments or discussions are valuable indicators of users' preferences. Unlike previous works which focus on labeled data from user-contributed reviews, we focus here on user comments which are not accompanied by explicit rating labels. We investigate their utility for a one-class collaborative filtering task such as bookmarking, where only the user actions are given as ground truth. We propose a sentiment-aware nearest neighbor model (SANN) for multimedia recommendations over TED talks, which makes use of user comments. The model outperforms significantly, by more than 25% on unseen data, several competitive baselines.
Keywords
Computer Science
Recognizing contextual polarity in phrase-level sentiment analysis
3,379 Citations2005Theresa Wilson, Janyce Wiebe +1 more
A new approach to phrase-level sentiment analysis is presented that first determines whether an expression is neutral or polar and then disambiguates the polarity of the polar expressions.
Performance of recommender algorithms on top-n recommendation tasks
1,412 Citations2010Paolo Cremonesi, Yehuda Koren +1 more
An extensive evaluation of several state-of-the art recommender algorithms suggests that algorithms optimized for minimizing RMSE do not necessarily perform as expected in terms of top-N recommendation task, and new variants of two collaborative filtering algorithms are offered.
One-Class Collaborative Filtering
1,012 Citations2008Rong Pan, Y. Zhou +5 more
This paper considers the one-class problem under the CF setting, and proposes two frameworks to tackle OCCF, one based on weighted low rank approximation; the other based on negative example sampling.
Applied Physics Letters10.1162/153244302760200704
696 Citations2000
It is shown how to use notions of stability for learning algorithms to derive generalization error bounds based on the empirical error and the leave-one-out error to apply to SVM for regression and classification.
Beyond the Stars: Improving Rating Predictions using Review Text Content.
400 Citations2009Gayatree Ganu, Noémie Elhadad +1 more
This paper proposes new ad-hoc and regression-based recommendation measures, that both take into account the textual component of user reviews, and shows that using textual information results in better general or personalized review score predictions than those derived from the numerical star ratings given by the users.
World Wide WebA probabilistic rating inference framework for mining user preferences from reviews
60 Citations2011Cane Wing-ki Leung, Stephen Chan +2 more
A novel Probabilistic Rating infErence Framework, known as Pref, for mining user preferences from reviews and then mapping such preferences onto numerical rating scales is proposed and a useful application of Pref as a rating inference framework is described.
Combining content with user preferences for TED lecture recommendation
26 Citations2013Nikolaos Pappas, Andréi Popescu-Belis
A new dataset is introduced and several methods for the recommendation of non-fiction audiovisual material, namely lectures from the TED website, are compared and the proposed combined method shows acceptable performances, and can be used in both settings.
Recommendation in Internet Forums and Blogs
20 Citations2010Jia Wang, Qing Li +2 more
This article presents a framework to recommend relevant information in Internet forums and blogs using user comments, one of the most representative of user behaviors in online discussion, and considers structural, semantic, and authority information carried by them.
Lecture notes in computer scienceDistinguishing the Popularity between Topics: A System for Up-to-Date Opinion Retrieval and Mining in the Web
18 Citations2013Nikolaos Pappas, Georgios Katsimpras +1 more
An online system for opinion mining and retrieval that is able to discover up-to-date web pages on given topics using focused crawling agents, extract opinionated textual parts from web pages, and estimate their polarity using opinion mining agents is presented.
Recommendations Based on User-Generated Comments in Social Media
16 Citations2011Andrew G. Messenger, Jon Whittle
It is shown that user comments allow recommender systems to track an evolving conversation related to a news article and can thus provide recommendations that better match the topics of conversation in comments, which maybe quite different from those in the original news article.
Augmenting Chinese Online Video Recommendations by Using Virtual Ratings Predicted by Review Sentiment Classification
15 Citations2010Weishi Zhang, Guiguang Ding +2 more
A new recommender algorithm is proposed by fusing a self-supervised emoticon-integrated sentiment classification approach, by which the missing User-Item Rating Matrix can be substituted by the virtual ratings which are predicted by decomposing user reviews as given to the items.