Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Retrieving collocations from text: Xtract
Computational Linguistics - Special issue on using large corpora: I
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
Unsupervised prediction of citation influences
Proceedings of the 24th international conference on Machine learning
Joint latent topic models for text and citations
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of activity patterns using topic models
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
AVA: Adjective-Verb-Adverb Combinations for Sentiment Analysis
IEEE Intelligent Systems
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Incorporating domain knowledge into topic modeling via Dirichlet Forest priors
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Topic Detection for Discussion Threads with Domain Knowledge
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Modeling user hidden navigational behavior for Web recommendation
Web Intelligence and Agent Systems
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Task knowledge based retrieval for service relevant to mobile user's activity
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Discovering geographical topics in the twitter stream
Proceedings of the 21st international conference on World Wide Web
Automatic task-based profile representation for content-based recommendation
International Journal of Knowledge-based and Intelligent Engineering Systems
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A topic model capable of assigning word pairs to associated topics is developed to explore people's activities. Considering that the form of word pairs led by verbs is a more effective way to express people's activities than separate words, we incorporate the word-connection model into the smoothed Latent Dirichlet Allocation LDA to ensure that the words are well paired and assigned to the associated topics. To quantitatively and qualitatively evaluate the proposed model, two datasets were built using Twitter posts as data sources: the wish-related and the geographical information-related datasets. The experiment results using the wish-related dataset indicate that the relatedness of words plays a key role in forming reasonable pairs, and the proposed model, word-pair generative Latent Dirichlet Allocation wpLDA, performs well in clustering. Results obtained using the geographical information-related dataset demonstrate that the proposed model works well for discovering people's activities, in which the activities are understandably represented with an intuitive character.