Open AccessProceedings Article
Exploiting coherence for the simultaneous discovery of latent facets and associated sentiments
Himabindu Lakkaraju,Chiranjib Bhattacharyya,Indrajit Bhattacharya,Srujana Merugu +3 more
- pp 498-509
TLDR
This work is the first attempt to combine the notions of syntactic and semantic dependencies in the domain of review mining by jointly discovering latent facets and sentiment topics, and also order the sentiment topics with respect to a multi-point scale, in a language and domain independent manner.Abstract:
Facet-based sentiment analysis involves discovering the
latent facets, sentiments and their associations. Traditional facet-based sentiment analysis algorithms typically perform the various tasks in sequence, and fail to take advantage of the mutual reinforcement of the tasks. Additionally,inferring sentiment levels typically requires domain knowledge or human intervention. In this paper, we propose aseries of probabilistic models that jointly discover latent facets and sentiment topics, and also order the sentiment topics with respect to a multi-point scale, in a language and domain independent manner. This is achieved by simultaneously capturing both short-range syntactic structure and long range semantic dependencies between the sentiment and facet words. The models further incorporate coherence in reviews, where reviewers dwell on one facet or sentiment level before moving on, for more accurate facet and sentiment discovery. For reviews which are supplemented with ratings, our models automatically order the latent sentiment topics, without requiring seed-words or domain-knowledge. To the best of our knowledge, our work is the first attempt to combine the notions of syntactic and semantic dependencies
in the domain of review mining. Further, the concept of
facet and sentiment coherence has not been explored earlier either. Extensive experimental results on real world review data show that the proposed models outperform various state of the art baselines for facet-based sentiment analysis.read more
Citations
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Book
Sentiment Analysis and Opinion Mining
TL;DR: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language as discussed by the authors and is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining.
Journal ArticleDOI
Survey on Aspect-Level Sentiment Analysis
Kim Schouten,Flavius Frasincar +1 more
TL;DR: An in-depth overview of the current state-of-the-art of aspect-level sentiment analysis is given, showing the tremendous progress that has been made in finding both the target, which can be an entity as such, or some aspect of it, and the corresponding sentiment.
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Sentiment analysis on social media for stock movement prediction
TL;DR: This paper shows an evaluation of the effectiveness of the sentiment analysis in the stock prediction task via a large scale experiment and a novel method for predicting stock price movement using the sentiment from social media.
Proceedings ArticleDOI
PhraseRNN: Phrase Recursive Neural Network for Aspect-based Sentiment Analysis
Thien Hai Nguyen,Kiyoaki Shirai +1 more
TL;DR: A new method is presented that takes both dependency and constituent trees of a sentence into account and significantly outperforms previous methods to identify sentiment of an aspect of an entity.
Proceedings ArticleDOI
Topic Modeling based Sentiment Analysis on Social Media for Stock Market Prediction
Thien Hai Nguyen,Kiyoaki Shirai +1 more
TL;DR: The results show that incorporation of the sentiment information from social media can help to improve the stock prediction.
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