Weakly Supervised Joint Sentiment-Topic Detection from Text
IEEE Transactions on Knowledge and Data EngineeringPublished 11 February 2011
Chenghua Lin, Yulan He, Richard Everson, Stefan Rüger
Citations312
SJR quartileQ1
SJR score2.57
SNIP3.30
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
It is hypothesized that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion and outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents.
Abstract
Copyright © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords
Computer Science
Journal of Machine Learning ResearchLatent dirichlet allocation
27,049 Citations2003David M. Blei, Andrew Y. Ng +1 more
Thumbs up?
6,987 Citations2002Bo Pang, Lillian Lee +1 more
This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.
now publishers, Inc. eBooksOpinion Mining and Sentiment Analysis
6,745 Citations2008Bo Pang, Lillian Lee
Foundations and Trends® in Information RetrievalOpinion Mining and Sentiment Analysis
6,204 Citations2008Bo Pang, Lillian Lee
This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.
Communications of the ACMProbabilistic topic models
5,549 Citations2012David M. Blei
Surveying a suite of algorithms that offer a solution to managing large document archives suggests they are well-suited to handle large amounts of data.
Probabilistic latent semantic indexing
3,916 Citations1999Thomas Hofmann
Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data.
Meeting of the Association for Computational LinguisticsThumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
3,654 Citations2002Peter Peter, Turney
A sentimental education
3,343 Citations2004Bo Pang, Lillian Lee
A novel machine-learning method is proposed that applies text-categorization techniques to just the subjective portions of the document, which greatly facilitates incorporation of cross-sentence contextual constraints.
Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
2,026 Citations2007John Blitzer, Mark Dredze +1 more
This work extends to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30% over the original SCL algorithm and 46% over a supervised baseline.
Labeled LDA
1,208 Citations2009Daniel Ramage, David Hall +2 more
Labeled LDA is introduced, a topic model that constrains Latent Dirichlet Allocation by defining a one-to-one correspondence between LDA's latent topics and user tags that allows Labeled LDA to directly learn word-tag correspondences.
Probabilistic Topic Models
1,031 Citations2015Mark Steyvers, Thomas L. Griffiths
Joint sentiment/topic model for sentiment analysis
972 Citations2009Chenghua Lin, Yulan He
A novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text is proposed, which is fully unsupervised.
ACM Transactions on Information SystemsSentiment analysis in multiple languages
894 Citations2008Ahmed Abbasi, Hsinchun Chen +1 more
Stylistic features significantly enhanced performance across all testbeds while EWGA also outperformed other feature selection methods, indicating the utility of these features and techniques for document-level classification of sentiments.
Topic sentiment mixture
813 Citations2007Qiaozhu Mei, Xu Ling +3 more
The proposed Topic-Sentiment Mixture (TSM) model can reveal the latent topical facets in a Weblog collection, the subtopics in the results of an ad hoc query, and their associated sentiments and could also provide general sentiment models that are applicable to any ad hoc topics.
Modeling online reviews with multi-grain topic models
792 Citations2008Ivan Titov, Ryan McDonald
This paper presents a novel framework for extracting ratable aspects of objects from online user reviews and argues that multi-grain models are more appropriate for this task since standard models tend to produce topics that correspond to global properties of objects rather than aspects of an object that tend to be rated by a user.
Estimating a Dirichlet Distribution
778 Citations2000Thomas P. Minka
Computational IntelligenceSENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS
750 Citations2006Alistair Kennedy, Diana Inkpen
It is shown that extending the term‐counting method with contextual valence shifters improves the accuracy of the classification, and combining the two methods achieves better results than either method alone.
A Joint Model of Text and Aspect Ratings for Sentiment Summarization
638 Citations2008Ivan Titov, Ryan McDonald
A statistical model is proposed which is able to discover corresponding topics in text and extract textual evidence from reviews supporting each of these aspect ratings, a fundamental problem in aspect-based sentiment summarization.
ScholarWorks@UMassAmherst (University of Massachusetts Amherst)Rethinking LDA: Why Priors Matter
563 Citations2009Hanna Wallach, David Mimno +1 more
The prior structure advocated substantially increases the robustness of topic models to variations in the number of topics and to the highly skewed word frequency distributions common in natural language.
Using appraisal groups for sentiment analysis
562 Citations2005Casey Whitelaw, Navendu Garg +1 more
A new method for sentiment classification based on extracting and analyzing appraisal groups such as ``very good'' or ``not terribly funny'' is presented, based on several task-independent semantic taxonomies based on Appraisal Theory.
Recent Advances in Natural Language ProcessingCustomizing Sentiment Classifiers to New Domains: a Case Study
370 Citations2005Anthony Aue, Michael Gamon
Four different approaches to customizing a sentiment classification system to a new target domain in the absence of large amounts of labeled data are surveyed and their advantages, disadvantages and performance are discussed.
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification
334 Citations2008Simon Lacoste-Julien, Fei Sha +1 more
This paper presents DiscLDA, a discriminative variation on Latent Dirichlet Allocation in which a class-dependent linear transformation is introduced on the topic mixture proportions, and obtains a supervised dimensionality reduction algorithm that uncovers the latent structure in a document collection while preserving predictive power for the task of classification.
Structured Models for Fine-to-Coarse Sentiment Analysis
286 Citations2007Ryan McDonald, Kerry Hannan +3 more
Experiments show that this structured model for jointly classifying the sentiment of text at varying levels of granularity can significantly reduce classification error relative to models trained in isolation.
A non-negative matrix tri-factorization approach to sentiment classification with lexical prior knowledge
188 Citations2009Tao Li, Yi Zhang +1 more
A novel approach to learn from lexical prior knowledge in the form of domain-independent sentiment-laden terms, in conjunction with domain-dependent unlabeled data and a few labeled documents is proposed.
When Specialists and Generalists Work Together: Overcoming Domain Dependence in Sentiment Tagging
165 Citations2008Alina Andreevskaia, Sabine Bergler
A sentiment annotation system that integrates a corpus-based classifier trained on a small set of annotated in-domain data and a lexicon-based system trained on WordNet, and a new system consisting of the ensemble of two classifiers with precision-based vote weighting that provides significant gains in accuracy and recall.
Multi-domain sentiment classification
134 Citations2008Shoushan Li, Chengqing Zong
This paper addresses a new task in sentiment classification that aims to improve performance through fusing training data from multiple domains simultaneously, and proposes two approaches of fusion, feature-level and classifier-level, to use training data for multi-domain sentiment classification.
Automatic construction of polarity-tagged corpus from HTML documents
92 Citations2006Nobuhiro Kaji, Masaru Kitsuregawa
A novel method of building polarity-tagged corpus from HTML documents that can be applied to arbitrary HTML documents and utilize certain layout structures and linguistic pattern to automatically extract sentences that express opinion.
Topic-wise, sentiment-wise, or otherwise?
51 Citations2009Sajib Dasgupta, Vincent Ng
A novel way of incorporating user feedback into a clustering algorithm is proposed, which allows a user to easily specify the dimension along which she wants the data points to be clustered via inspecting only a small number of words.