Papers with sentiment classifiers
On Positivity Bias in Negative Reviews (2021.acl-short)
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| Challenge: | Existing studies have shown positive words are more frequently used in negative reviews . however, it remains unclear whether the Pollyanna hypothesis holds in negative review . |
| Approach: | They validate the Pollyanna hypothesis that positive words occur more frequently than negative words in human expressions . they use a variety of review datasets to examine the use of positive and negative words . |
| Outcome: | The results confirm the pollyanna hypothesis that positive words occur more frequently than negative words in human expressions. |
Resource Creation Towards Automated Sentiment Analysis in Telugu (a low resource language) and Integrating Multiple Domain Sources to Enhance Sentiment Prediction (L18-1)
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| Challenge: | Sentiment Analysis of text is an important task in many applications . but the task becomes challenging when it comes to low resource languages . |
| Approach: | They propose to create a corpus of polarity-based sentiment classifiers in Telugu for different domains like movie reviews, song lyrics, product reviews and book reviews. |
| Outcome: | The proposed model performs well in multiple domains and is compared with the previous models. |
Pretraining Sentiment Classifiers with Unlabeled Dialog Data (P18-2)
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| Challenge: | Existing methods to train sentiment classifiers with unlabeled data are costly and time-consuming. |
| Approach: | They propose a conditional language model with unlabeled dialog data instead of a language model to pretrain sentiment classifiers. |
| Outcome: | The proposed strategy outperforms state-of-the-art methods with unlabeled dialog data and is simple but effective. |
The Effect of Round-Trip Translation on Fairness in Sentiment Analysis (2021.emnlp-main)
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| Challenge: | Sentiment analysis systems exhibit sensitivity to protected attributes, while round-trip translation has been shown to normalize text. |
| Approach: | They propose to use round-trip translation to normalize text to reduce the fairness gap between groups in sentiment analysis. |
| Outcome: | The proposed method reduces the fairness gap between groups by up to 47%. |