Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid

By Wayne Xin Zhao, Jing Jiang, Hongfei Yan, Xiaoming Li. 2010

449 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. B. Liu
2. Bing Liu
3. Charu C. Aggarwal
4. Philip S. Yu
5. Hu Xu
6. Erik Cambria
7. Soujanya Poria
8. Qiang Yang

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].”
• 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].”
• Aspect extraction for opinion mining with a deep convolutional neural network (2016, methodology): “(Zhao et al., 2010) employed the maximum entropy method to train a switch variable based on POS tags of words and used it to separate aspect and sentiment words.”
• Explainable recommendation with fusion of aspect information (2018, background): “By LDA, we obtain the topics and their corresponding topic words, and we regard each topic in LDA as an aspect.”
• An Unsupervised Neural Attention Model for Aspect Extraction (2017, methodology): “Following (Brody and Elhadad, 2010; Zhao et al., 2010), we set the number of aspects for the restaurant corpus to 14.”
• Survey on Aspect-Level Sentiment Analysis (2016, methodology): “(2010) [76] restaurant [77] & ternary + aspect identification avg.”
• 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, 20”
• Cross-Domain Co-Extraction of Sentiment and Topic Lexicons (2012, background): “Some researchers also proposed to use topic modeling to identify implicit topics and sentiment words (Mei et al., 2007; Titov and Mc-Donald, 2008; Zhao et al., 2010; Li et al., 2010b), where a topic is a cluster of words, which is different from our fine-grained topic-word extraction.”
• Latent aspect rating analysis without aspect keyword supervision (2011, methodology): “, using supervision from sentiment priors [12, 9] or supervision from labeled sentences [24].”
• A Survey of Opinion Mining and Sentiment Analysis (2012, methodology): “[119] proposed a MaxEnt-LDA hybrid model to jointly discover both aspect words and aspect-specific opinion words, which can leverage syntactic features to help separate aspects”
• Mining topics in documents: standing on the shoulders of big data (2014, methodology): “Topic models have been used for the task by many researchers [5, 10, 16, 21, 23, 25, 26, 32, 33, 37].”
• Mining Aspect-Specific Opinion using a Holistic Lifelong Topic Model (2016, methodology): “sions), they are not specialized models nor fine-grained models for opinion mining as [25, 19, 52].”
• FLAME: A Probabilistic Model Combining Aspect Based Opinion Mining and Collaborative Filtering (2015, background): “For the process of generating words, we follow the assumption in [20, 13, 25] that the words in one sentence of a review refer to the same aspect.”
• 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.”
• Topic Modeling based Sentiment Analysis on Social Media for Stock Market Prediction (2015, methodology): “MaxEnt-LDA hybrid model can jointly discover both aspects and aspect-specific opinion words on a restaurant review dataset (Zhao et al., 2010), while FACTS, CFACTS, FACTS-R, and CFACTS-R model were proposed for sentiment analysis on a product review data (Lakkaraju et al., 2011).”
• Aspect Extraction through Semi-Supervised Modeling (2012, methodology): “Our models are related to topic models in general (Blei et al., 2003) and joint models of aspects and sentiments in sentiment analysis in specific (e.g., Zhao et al., 2010).”