The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
ICML '06 Proceedings of the 23rd international conference on Machine learning
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
Extracting the discussion structure in comments on news-articles
Proceedings of the 9th annual ACM international workshop on Web information and data management
Proceedings of the 17th international conference on World Wide Web
Topic modeling with network regularization
Proceedings of the 17th international conference on World Wide Web
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Comments-oriented document summarization: understanding documents with readers' feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A survey of Web clustering engines
ACM Computing Surveys (CSUR)
Ranking Comments on the Social Web
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Joint sentiment/topic model for sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Exploiting internal and external semantics for the clustering of short texts using world knowledge
Proceedings of the 18th ACM conference on Information and knowledge management
Predicting the volume of comments on online news stories
Proceedings of the 18th ACM conference on Information and knowledge management
Hierarchical Bayesian Models for Collaborative Tagging Systems
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
How useful are your comments?: analyzing and predicting youtube comments and comment ratings
Proceedings of the 19th international conference on World wide web
The topic-perspective model for social tagging systems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Latent aspect rating analysis on review text data: a rating regression approach
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Content without context is meaningless
Proceedings of the international conference on Multimedia
Aspect and sentiment unification model for online review analysis
Proceedings of the fourth ACM international conference on Web search and data mining
Transferring topical knowledge from auxiliary long texts for short text clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
Comment spam detection by sequence mining
Proceedings of the fifth ACM international conference on Web search and data mining
Topical clustering of search results
Proceedings of the fifth ACM international conference on Web search and data mining
Multi-objective ranking of comments on web
Proceedings of the 21st international conference on World Wide Web
Care to comment?: recommendations for commenting on news stories
Proceedings of the 21st international conference on World Wide Web
Leveraging user comments for aesthetic aware image search reranking
Proceedings of the 21st international conference on World Wide Web
Short text classification improved by learning multi-granularity topics
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Going beyond Corr-LDA for detecting specific comments on news & blogs
Proceedings of the 7th ACM international conference on Web search and data mining
Hi-index | 0.00 |
Readers of a news article often read its comments contributed by other readers. By reading comments, readers obtain not only complementary information about this news article but also the opinions from other readers. However, the existing ranking mechanisms for comments (e.g., by recency or by user rating) fail to offer an overall picture of topics discussed in comments. In this paper, we first propose to study Topic-driven Reader Comments Summarization (Torcs) problem. We observe that many news articles from a news stream are related to each other; so are their comments. Hence, news articles and their associated comments provide context information for user commenting. To implicitly capture the context information, we propose two topic models to address the Torcs problem, namely, Master-Slave Topic Model (MSTM) and Extended Master-Slave Topic Model (EXTM). Both models treat a news article as a master document and each of its comments as a slave document. MSTM model constrains that the topics discussed in comments have to be derived from the commenting news article. On the other hand, EXTM model allows generating words of comments using both the topics derived from the commenting news article, and the topics derived from all comments themselves. Both models are used to group comments into topic clusters. We then use two ranking mechanisms Maximal Marginal Relevance (MMR) and Rating & Length (RL) to select a few most representative comments from each comment cluster. To evaluate the two models, we conducted experiments on 1005 Yahoo! News articles with more than one million comments. Our experimental results show that EXTM significantly outperforms MSTM by perplexity. Through a user study, we also confirm that the comment summary generated by EXTM achieves better intra-cluster topic cohesion and inter-cluster topic diversity.