Topic sentiment mixture: modeling facets and opinions in weblogs

By Qiaozhu Mei, Xu Ling, Matthew Wondra, Hang Su, Chengxiang Zhai. 2007

884 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Joint Topic-Semantic-aware Social Recommendation for Online Voting (2017, neutral): “LDA is widely used in sentiment analysis [20], aspects and opinions mining [33], and recommendation [5].”
Lifelong Learning CRF for Supervised Aspect Extraction (2017, methodology): “…extraction (Zhuang et al., 2006; Wang and Wang, 2008; Wu et al., 2009; Zhang et al., 2010; Qiu et al., 2011; Poria et al., 2014), topic modeling (Mei et al., 2007; Titov and McDonald, 2008; Li et al., 2010; Brody and Elhadad, 2010; Wang et al., 2010; Moghaddam and Ester, 2011; Mukherjee and…”
Weakly Supervised Joint Sentiment-Topic Detection from Text (2012, methodology): “Although work in this line is still relatively sparse, some studies have preserved a similar vision [11], [12], [20].”
A Latent Variable Model for Geographic Lexical Variation (2010, background): “Mei et al. (2007) include sentiment as a second dimension in a topic model, using a switching variable so that individual word tokens may be selected from either the topic or the sentiment.”
A survey on sentiment detection of reviews (2009, methodology): “Pang et al. (2002) applied Naı̈ve Bayes, Maximum Entropy and Support Vector Machine classification techniques to the identification of the polarity of movie reviews.”
Structure-Aware Review Mining and Summarization (2010, background): “Recently, there are some studies on joint sentiment/topic extraction (Mei et al. 2007; Titov and McDonald, 2008; Snyder and Barzilay, 2007).”
Cross-Lingual Latent Topic Extraction (2010, background): “, 2004), contextual topic analysis (Mei and Zhai, 2006), dynamic and correlated topic models (Blei and Lafferty, 2005; Blei and Lafferty, 2006), and opinion analysis (Mei et al., 2007; Branavan et al., 2008).”
Topic Modeling Using Latent Dirichlet allocation (2021, methodology): “Topic-Sentiment Mixture (TSM) was proposed to model the topic in the Weblog archive [92].”
A Survey of Opinion Mining and Sentiment Analysis (2012, methodology): “[67] proposed to use a positive sentiment model and a negative sentiment model in additional to aspect models.”
Exploiting Topic based Twitter Sentiment for Stock Prediction (2013, methodology): “LDA can learn a predefined number of topics and has been widely applied in its extended forms in sentiment analysis and many other tasks (Mei et al., 2007; Branavan et al., 2008; Lin and He, 2009; Zhao et al., 2010; Wang et al., 2010; Brody and Elhadad, 2010; Jo and Oh, 2011; Moghaddam and Ester,…”
Aspect Extraction through Semi-Supervised Modeling (2012, background): “…(i.e., discovering positive and negative aspect words and/or sentiments for each aspect without separating aspect and sentiment terms) (Lin and He, 2009; Brody and Elhadad, 2010, Jo and Oh, 2011) and separately discovering both aspects and sentiments (e.g., Mei et al., 2007; Zhao et al., 2010).”
A Joint Model of Text and Aspect Ratings for Sentiment Summarization (2008, background): “Text excerpts are usually extracted through string matching (Hu and Liu, 2004a; Popescu and Etzioni, 2005), sentence clustering (Gamon et al., 2005), or through topic models (Mei et al., 2007; Titov and McDonald, 2008).”
Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news (2014, background): “Other studies, such as [16,17,40], have used different approaches to handle topic and sentiment, and [35], for example, detected sentiment simultaneously with (latent) topics while utilizing the Latent Dirichlet Allocation (LDA) method [3].”
SAR: A sentiment-aspect-region model for user preference analysis in geo-tagged reviews (2015, background): “Studies on phrase level sentiment [13], [20], [12], [15], [30] use NLP tools to analyze the dependency between the words in a sentence and extract aspect-opinion phrase pairs, e.”
Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS) (2014, methodology): “Different from a semi-supervised component or opinion lexicon used in [14, 26], our sentiments are learnt by building a linkage between user ratings and sentiments.”
From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series (2010, background): “If there is enough training data, this could be formulated as a topic-sentiment model (Mei et al. 2007), in which the topics and sentiment of documents are jointly inferred.”