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Emotion dataset from Indonesian public opinion.

Riccosan1, Karen Etania Saputra1, Galih Dea Pratama1

  • 1Computer Science Department, Binus Graduate Program - Master of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia.

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This study created a new Indonesian tweet dataset for emotion classification. The dataset, containing six emotions, addresses the lack of resources for Indonesian sentiment analysis.

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Social Media Analysis

Background:

  • Public opinions and emotional responses are abundant on social media platforms like Twitter.
  • Indonesia's large and active Twitter user base presents a significant opportunity for opinion data mining.
  • Existing emotion text datasets in Indonesian are limited, highlighting a research gap.

Purpose of the Study:

  • To develop a comprehensive dataset of Indonesian-language tweets for emotion classification.
  • To facilitate research in understanding and analyzing public sentiment and emotions expressed in Indonesian text.
  • To address the scarcity of labeled emotion datasets for the Indonesian language.

Main Methods:

  • Collected Indonesian tweets from Twitter to form a dataset of public opinions.
  • Pre-processed the gathered data through cleaning and normalization for subsequent analysis.
  • Annotated the dataset with six distinct emotional labels: anger, fear, joy, love, sad, and neutral.

Main Results:

  • Successfully created a novel dataset of Indonesian tweets focused on public opinion and emotions.
  • The dataset is prepared for the task of classifying emotions in Indonesian text.
  • The annotated data provides a valuable resource for computational emotion analysis in Indonesian.

Conclusions:

  • The developed Indonesian emotion dataset fills a critical gap in available resources.
  • This dataset will enable advancements in sentiment analysis and emotion detection for the Indonesian language.
  • The research supports the potential of social media data mining for understanding public emotional responses.