Automated Author ProfileMartianus Henry, Matthew
Bina Nusantara University
Martianus Henry, Matthew
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 3.2 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The EmoTweetID dataset is a publicly available resource of Indonesian tweets collected from X (formerly Twitter) using emotion-related keywords. The dataset consists of three main components:1. EmoTweetID-Corpus.csv: 3,126,987 unlabeled tweets for unsupervised tasks such as word embedding construction.2. EmoTweetID-Lexicon.csv: 2,243 tweets automatically annotated using the Indonesian NRC EmoLex.3. EmoTweetID-Human.csv: 2,243 tweets manually annotated by three psychology students, with inter-annotator agreement measured using Cohen’s and Fleiss’ Kappa.Both annotated files (EmoTweetID-Lexicon.csv and EmoTweetID-Human.csv) provide labels following Ekman’s six basic emotions: anger, disgust, fear, joy, sadness, and surprise.Additionally, two pre-trained word embedding models (Wors2Vec and FastText) trained on the corpus, TweetID-Word2Vec.zip and TweetID-FastText.zip, are provided for various downstream NLP tasks.This dataset offers a valuable benchmark for affective computing and natural language processing in Indonesian, supporting research in emotion recognition, social media analysis, and the development of empathetic AI systems.
Authors
- Setyo Nugroho, Kuncahyo ;
- Abdurrachman Bachtiar, Fitra ;
- Firdaus Mahmudy, Wayan ;
- Martianus Henry, Matthew ;
- Isnan, Mahmud ;
- Pangestu, Gusti ;
- Pardamean, Bens
The EmoTweetID dataset is a publicly available resource of Indonesian tweets collected from X (formerly Twitter) using emotion-related keywords. The dataset consists of three main components:1. EmoTweetID-Corpus.csv: 3,126,987 unlabeled tweets for unsupervised tasks such as word embedding construction.2. EmoTweetID-Lexicon.csv: 2,243 tweets automatically annotated using the Indonesian NRC EmoLex.3. EmoTweetID-Human.csv: 2,243 tweets manually annotated by three psychology students, with inter-annotator agreement measured using Cohen’s and Fleiss’ Kappa.Both annotated files (EmoTweetID-Lexicon.csv and EmoTweetID-Human.csv) provide labels following Ekman’s six basic emotions: anger, disgust, fear, joy, sadness, and surprise.Additionally, two pre-trained word embedding models (Wors2Vec and FastText) trained on the corpus, TweetID-Word2Vec.zip and TweetID-FastText.zip, are provided for various downstream NLP tasks.This dataset offers a valuable benchmark for affective computing and natural language processing in Indonesian, supporting research in emotion recognition, social media analysis, and the development of empathetic AI systems.
Authors
- Setyo Nugroho, Kuncahyo ;
- Abdurrachman Bachtiar, Fitra ;
- Firdaus Mahmudy, Wayan ;
- Martianus Henry, Matthew ;
- Isnan, Mahmud ;
- Pangestu, Gusti ;
- Pardamean, Bens