Papers with sentiment polarity classification
A Helping Hand: Transfer Learning for Deep Sentiment Analysis (P18-1)
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| Challenge: | Existing deep neural models for sentiment polarity classification require large amounts of training data. |
| Approach: | They propose to feed generic cues into the training process of deep convolutional neural networks for sentiment analysis. |
| Outcome: | The proposed approach improves sentiment polarity classification on a range of datasets in seven languages. |
Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model (2021.emnlp-main)
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| Challenge: | Existing models focus on aspect term extraction, opinion term extraction and sentiment polarity classification but ignore the difference. |
| Approach: | They propose a joint aspect-based sentiment analysis task that focuses on the difference between the two tasks to improve the model's robustness. |
| Outcome: | Empirical results show that the proposed model outperforms the previous state-of-the-art on four benchmark datasets. |
Span-level Aspect-based Sentiment Analysis via Table Filling (2023.acl-long)
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| Challenge: | Existing methods to analyze aspect-based sentiment analysis focus on word-level dependencies between aspect and opinion expressions. |
| Approach: | They propose a span-level ABSA model which considers consistency of multi-word opinion expressions at the span- level. |
| Outcome: | The proposed model can be used to identify the sentiment polarity of a given aspect . it is based on a table filling method and a regularizer to guarantee consistency . |
SOUL: Towards Sentiment and Opinion Understanding of Language (2023.emnlp-main)
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| Challenge: | Sentiment analysis models often fail to capture the broader complexities of sentiment analysis. |
| Approach: | They propose a task to evaluate sentiment understanding through two subtasks . they annotate a new dataset comprising 15,028 statements from 3,638 reviews . |
| Outcome: | The proposed task evaluates sentiment understanding through two subtasks . it is a challenging task for both small and large language models, with performance gaps of up to 27% . |
A Japanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain (2022.lrec-1)
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Haruya Suzuki, Yuto Miyauchi, Kazuki Akiyama, Tomoyuki Kajiwara, Takashi Ninomiya, Noriko Takemura, Yuta Nakashima, Hajime Nagahara
| Challenge: | Existing studies on emotion analysis have studied the analysis of basic emotions and sentiment polarity independently. |
| Approach: | They extend the WRIME dataset with basic emotion intensity from both the writer's subjective and reader's perspective to include the Japanese sentiment polarity. |
| Outcome: | The proposed dataset is the first large-scale corpus to annotate both basic emotions and sentiment polarity labels from both the writer’s and reader’s perspectives. |