Ireland

Public opinion in social media is increasingly becoming a critical factor in pandemic control. Understanding the emotions of a population towards vaccinations and COVID-19 may be valuable in convincing members to become vaccinated. We investigated the emotions of Japanese Twitter users towards Tweets related to COVID-19 vaccination. Using the WRIME dataset, which provides emotion ratings for Japanese Tweets sourced from writers (Tweet posters) and readers, we fine-tuned a BERT model to predict levels of emotional intensity. This model achieved a training accuracy of MSE = 0.356. A separate dataset of 20,254 Japanese Tweets containing COVID-19 vaccine-related keywords was also collected, on which the fine-tuned BERT was used to perform emotion analysis. Afterwards, a correlation analysis between the extracted emotions and a set of vaccination measures in Japan was conducted. The results revealed that surprise and fear were the most intense emotions predicted by the model for writers and readers, respectively, on the vaccine-related Tweet dataset. The correlation analysis also showed that vaccinations were weakly positively correlated with predicted levels of writer joy, writer/reader anticipation, and writer/reader trust. Code will be made available at https: //github.com/PatrickJohnRamos/BERT-Japan-vaccination.

ACL 2022

Emotion Analysis of Writers and Readers of Japanese Tweets on Vaccinations

vaccination

twitter

emotion analysis

bert

**Welcome to ACL-IJCNLP 2021!**

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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

workshop paper

# Welcome everyone to ACL 2022!

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The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

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Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction

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Assessment of Massively Multilingual Sentiment Classifiers

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Over the years, the review helpfulness prediction task has been the subject of several works, but remains being a challenging issue in Natural Language Processing, as results vary a lot depending on the domain, on the adopted features and on the chosen classification strategy. This paper attempts to evaluate the impact of
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Our system, IUCL, participated in the WASSA 2022 Shared Task on Empathy Detection and Emotion Classification. Our main goal in building this system is to investigate how the use of demographic attributes influences performance. Our (official) results show that our text-only systems perform very competitively, ranking first in the empathy detection task, reaching an average Pearson correlation of 0.54, and second in the emotion classification task, reaching a Macro-F of 0.572. Our systems that use both text and demographic data are less competitive.

IUCL at WASSA 2022 Shared Task: A Text-only Approach to Empathy and Emotion Detection

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Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification

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Leveraging Emotion-Specific features to improve Transformer performance for Emotion Classification

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BERT models to tackle this problem achieving an F1 score of 62.76%.

Transformer based ensemble for emotion detection

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Team IITP-AINLPML at WASSA 2022: Empathy Detection, Emotion Classification and Personality Detection

In this presentation, we adopt the prompt-based method with the pre-trained language model to accomplish the Personality Prediction (PER) and Reactivity Index Prediction (IRI) tasks. Specifically, the prompt is designed to provide knowledge of the extra personalized information for enhancing the pre-trained model. Data augmentation and model ensemble are adopted for obtaining better results. Extensive experiments are performed, which shows the effectiveness of the proposed method. On the final submission, our system achieves a Pearson Correlation Coefficient of 0.2301 and 0.2546 on Track 3 and Track 4 respectively. We ranked 1-st on both sub-tasks.

Emotion Analysis of Writers and Readers of Japanese Tweets on Vaccinations

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Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction

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Emotion Analysis of Writers and Readers of Japanese Tweets on Vaccinations

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Next from ACL 2022

Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction

Similar lecture

Stance Detection in COVID-19 Tweets

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads