Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews (2021.eacl-main)
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| Challenge: | Existing models for sentiment-topic extraction assume topics are grouped under discrete sentiment categories such as ‘positive’, ‘negative’ and ‘neural’. |
| Approach: | They propose a Brand-Topic Model which aims to detect brand-associated polarity-bearing topics from product reviews. |
| Outcome: | The proposed model outperforms existing models on Amazon reviews and shows that it is more coherent and unique than existing models. |
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| Challenge: | Existing models that infer brand polarity scores from reviews are not able to infer polarities directly. |
| Approach: | They propose a dynamic Brand-Topic Model which detects and tracks brand-associated sentiment scores and polarity-bearing topics from product reviews organized in temporally ordered time intervals. |
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| Challenge: | Existing topic models may extract topics associated with writers’ subjective opinions mixed with those related to factual descriptions. |
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| Challenge: | Existing models for user reviews are limited by data sparsity and lack of data. |
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| Challenge: | Using coherence scores to choose topics, we test whether the results help us to understand user interests and concerns. |
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| Challenge: | Current neural approaches to topic modeling have not been able to solve all of the problems. |
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Zongxia Li, Andrew Mao, Daniel Stephens, Pranav Goel, Emily Walpole, Alden Dima, Juan Fung, Jordan Boyd-Graber
| Challenge: | Existing evaluation metrics such as coherence and coherency are inadequate for neural topic models. |
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Yanan Ma, Chenghao Xiao, Chenhan Yuan, Sabine N Van Der Veer, Lamiece Hassan, Chenghua Lin, Goran Nenadic
| Challenge: | Existing topic modelling methods encode contextual information of documents while ignoring contextual details of candidate centroid words. Existing methods are limited by the contextualization gap. |
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