The Journal of Machine Learning Research
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Opinion extraction and summarization on the web
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Joint sentiment/topic model for sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Empirical study of topic modeling in Twitter
Proceedings of the First Workshop on Social Media Analytics
A Signal Denoising Method for Text Meaning Vectors
AMS '11 Proceedings of the 2011 Fifth Asia Modelling Symposium
Weakly Supervised Joint Sentiment-Topic Detection from Text
IEEE Transactions on Knowledge and Data Engineering
Ranked WordNet graph for Sentiment Polarity Classification in Twitter
Computer Speech and Language
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In this paper, we address the problem of opinion analysis using a probabilistic approach to the underlying structure of different types of opinions or sentiments around a certain object. In our approach, an opinion is partitioned according to whether there is a direct relevance to a latent topic or sentiment. Opinions are then expressed as a mixture of sentiment-related parameters and the noise is regarded as data stream errors or spam. We propose an entropy-based approach using a value-weighted matrix for word relevance matching which is also used to compute document scores. By using a bootstrap technique with sampling proportions given by the word scores, we show that a lower dimensionality matrix can be achieved. The resulting noise-reduced data is regarded as a sentiment-preserving reduction layer, where terms of direct relevance to the initial parameter values are stored