# Multi-aspect Sentiment Analysis with Topic Models

@article{Lu2011MultiaspectSA,
title={Multi-aspect Sentiment Analysis with Topic Models},
author={Bin Lu and Myle Ott and Claire Cardie and Benjamin Ka-Yin T'sou},
journal={2011 IEEE 11th International Conference on Data Mining Workshops},
year={2011},
pages={81-88}
}
• Published 1 December 2011
• Computer Science
• 2011 IEEE 11th International Conference on Data Mining Workshops
We investigate the efficacy of topic model based approaches to two multi-aspect sentiment analysis tasks: multi-aspect sentence labeling and multi-aspect rating prediction. For sentence labeling, we propose a weakly-supervised approach that utilizes only minimal prior knowledge -- in the form of seed words -- to enforce a direct correspondence between topics and aspects. This correspondence is used to label sentences with performance that approaches a fully supervised baseline. For multi-aspect…
243 Citations
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• 2013
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A topic refinement algorithm that enhances semantic interpretability of topics to match that of visually identifiable aspects is presented and it is shown that, with this refinement, topics elicited from cross-collection topic models align excellently with entity aspects.
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An Attention-driven Keywords Ranking (AKR) method, which can automatically discover aspect keywords and aspect-level opinion keywords from the review corpus based on the attention weights, is proposed, which is significant for rating predictions by FEDAR.
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2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC)
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This paper proposes a multi-task framework called multi-sentiment hierarchical attention network (MSHAN) that jointly performs document-level and multi-aspect sentiment classification both and shows that the proposed method outperforms previous methods.
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EMNLP
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A model of multiple-instance learning applied to the prediction of aspect ratings or judgments of specific properties of an item from user-contributed texts such as product reviews demonstrates interpretability and explanatory power for its predictions.
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This work develops a modification for several popular sentiment-related LDA extensions that trains prior hyperparameters $$\beta$$ for specific words, and shows how this approach leads to new aspect-specific lexicons of sentiment words based on a small set of “seed” sentiment words.

## References

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