Training a Broad-Coverage German Sentiment Classification Model for Dialog Systems (2020.lrec-1)
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| Challenge: | Existing sentiment data sets are not available for sentiment analysis. |
| Approach: | They propose to combine a German sentiment corpus with existing resources to train a general-purpose German sentiment classification model. |
| Outcome: | The proposed model trains a general-purpose German sentiment classification model . the data set contains 5.4 million labelled samples . |
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| Challenge: | The paper presents a new training dataset of sentences in 7 languages, manually annotated for sentiment, which is used in a series of experiments focused on training a robust sentiment identifier for parliamentary proceedings. |
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Data Augmentation for Multiclass Utterance Classification – A Systematic Study (2020.coling-main)
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| Challenge: | Recent studies have focused on identifying the sentiment polarity of aspects in product reviews. |
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Orith Toledo-Ronen, Roy Bar-Haim, Alon Halfon, Charles Jochim, Amir Menczel, Ranit Aharonov, Noam Slonim
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Shuai Fan, Chen Lin, Haonan Li, Zhenghao Lin, Jinsong Su, Hang Zhang, Yeyun Gong, JIan Guo, Nan Duan
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DENS: A Dataset for Multi-class Emotion Analysis (D19-1)
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| Challenge: | Existing methods for structured sentiment analysis (SSA) focus on subcomponents of sentiment graphs without explicitly expressing their relations or the polarity. |
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