Overview
This dataset addresses sentiment composition – predicting the sentiment of a phrase from the interaction between its constituents. For example, in the phrases “reduced bureaucracy” and “fresh injury”, both “reduced” and “fresh” are followed by a negative word. However, “reduced” flips the negative polarity, resulting in a positive phrase, while “fresh” propagates the negative polarity to the phrase level, resulting in a negative phrase. Accordingly, “reduced” is part of our “reversers” lexicon, and “fresh” is part of the “propagators” lexicon.
Dataset Metadata
| Field | Value |
|---|---|
| Format | XLSX TXT |
| License | CC-BY-SA 3.0 |
| Domain | Natural Language Processing |
| Number of Records | 2,783 words, 66,058 unigrams, 262,555 bigrams |
| Data Split | 2,783 words, 66,058 unigrams, 262,555 bigrams |
| Size | 10MB |
| Author | Orith Toledo-Ronen, Roy Bar-Haim, Charles Jochim, Noam Slonim, Ranit Aharonov |
| Dataset Origin | IBM Research Project Debater |
| Dataset Version | 1.0.2 |
| Data Coverage | N/A |
Dataset Archive Contents
| File or Folder | Description |
|---|---|
ReleaseNotes.docx |
release notes file describing the data |
SEMANTIC_CLASSES.xlsx |
the composition lexicons for reversers, propagators, and dominators |
ADJECTIVES.xlsx |
the composition lexicons for two gradable adjective pairs (high-low, fast-slow) and their expansion list |
LEXICON_UG.txt |
the unigrams sentiment lexicon |
LEXICON_BG.txt |
the bigrams sentiment lexicon |
Data Glossary and Preview
Click here to explore the data glossary, sample records, and additional dataset metadata.
Use the Dataset
This dataset is complemented by a data exploration and data analysis Python notebook to help you get started:
Related Links
- Project Debater Project Debater is the first AI system that can debate humans on complex topics. The goal is to help people build persuasive arguments and make well-informed decisions. This dataset contributed to training the models in Project Debater.
Citation
@article{sentiment_composition_lexicons,
author="Orith Toledo-Ronen
and Roy Bar-Haim
and Alon Halfon
and Amir Menczel
and Charles Jochim
and Noam Slonim
and Ranit Aharonov",
title="Learning Sentiment Composition from Sentiment Lexicons",
journal="COLING",
year="2018",
}
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