Joint sentiment/topic model for sentiment analysis

By Chenghua Lin, Yulan He. International Conference on Information and Knowledge Management, 2009

502 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a45973f8d5ede2bc700d3e6c0758e5c90455d5a0

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In Artificial Intelligence

Citations in the corpus

Aspect Extraction through Semi-Supervised Modeling (2012, background): “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.”
A review of natural language processing techniques for opinion mining systems (2017, methodology): “Probabilistic generative model based approaches Inspired by the LDA topic model, some generative models were proposed, including joint sentiment topic model [72] and dependency-sentimentLDA model [73], which model the transitions between sentiments of words with a Markov chain.”
SemEval-2015 Task 12: Aspect Based Sentiment Analysis (2015, methodology): “The available methods can be divided into those that adopt domain-independent solutions (Lin and He, 2009), and those that use domain-specific knowledge to improve their results (Thet et al., 2010).”
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].”
Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier (2016, background): “In paper [10], Chenghua Lin and Yulan He presented a joint sentiment/topic (JST) model.”
Latent aspect rating analysis without aspect keyword supervision (2011, methodology): “, using supervision from sentiment priors [12, 9] or supervision from labeled sentences [24].”
Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid (2010, background): “However, as suggested in (Mei et al., 2007; Lin and He, 2009), fully unsupervised topic models are unable to identify opinion words well.”
Exploiting Topic based Twitter Sentiment for Stock Prediction (2013, methodology): “…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, 2011; Sauper et al., 2011;…”
Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings (2015, methodology): “, 2009) and CRF (Shariaty and Moghaddam, 2011; Yang and Cardie, 2012; Yang and Cardie, 2013), as well as topic modeling techniques using Latent Dirichlet Allocation (LDA) model and its variants (Titov and McDonald, 2008; Lin and He, 2009; Moghaddam and Ester, 2012).”
Learning Attitudes and Attributes from Multi-aspect Reviews (2012, background): “A number of works model the relationship between aspects and opinions on aspects [21], [36].”
Topic Modeling based Sentiment Analysis on Social Media for Stock Market Prediction (2015, methodology): “JST model is used to extract topics and sentiments simultaneously.”
A Study of Information Retrieval Weighting Schemes for Sentiment Analysis (2010, methodology): “In contrast to machine learning approaches that require labeled corpora for training, Lin and He (2009) proposed an unsupervised probabilistic modeling framework, based on Latent Dirichlet Allocation (LDA).”
Applications of Topic Models (2017, background): “, 2007a], a prior learned from observed sentiment [Mimno and McCallum, 2008], or from a latent variable that can serve as a proxy for sentiment [Lin and He, 2009].”
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction (2018, background): “…and Etzioni, 2005), syntactic rules-based extraction (Zhuang et al., 2006; Wang and Wang, 2008; Qiu et al., 2011), topic modeling (Mei et al., 2007; Titov and McDonald, 2008; Lin and He, 2009; Moghaddam and Ester, 2011), word alignment (Liu et al., 2013) and label propagation (Zhou et al., 2013).”
Incorporating Structured Commonsense Knowledge in Story Completion (2018, background): “Additionally, there have been studies focused on joint learning for better performance, such as detecting sentiment and topic simultaneously from text (Lin and He 2009).”
Alleviating Data Sparsity for Twitter Sentiment Analysis (2012, methodology): “The joint sentiment-topic (JST) model [11] is a four-layer generative model which allows the detection of both sentiment and topic simultaneously from text.”
Survey on mining subjective data on the web (2011, background): “Alternatively, Mei et al. (2007) and Lin and He (2009) propose to include sentiment variables into a probabilistic topic inference model.”