# Non-Compositionality in Sentiment: New Data and Analyses

arXiv: https://arxiv.org/abs/2310.20656 · HTML: https://mlsanity.com/paper/2310.20656/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 17 of 17)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [TimeLMs: Diachronic Language Models from Twitter](https://mlsanity.com/paper/2202.03829/) (2022)
- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study](https://mlsanity.com/paper/2304.04339/) (2023)
- [TweetNLP: Cutting-Edge Natural Language Processing for Social Media](https://mlsanity.com/paper/2206.14774/) (2022)
- [SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics](https://mlsanity.com/paper/2005.04114/) (2020)
- [SemEval-2015 Task 10: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02387/) (2019)
- [Sentiment analysis is not solved! Assessing and probing sentiment classification](https://mlsanity.com/paper/1906.05887/) (2019)
- [Recursive Neural Networks with Bottlenecks Diagnose (Non-)Compositionality](https://mlsanity.com/paper/2301.13714/) (2023)
- [The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition](https://mlsanity.com/paper/1712.01794/) (2017)
- [Sentiment Composition of Words with Opposing Polarities](https://mlsanity.com/paper/1805.04542/) (2018)
- [Tell Me Why You Feel That Way: Processing Compositional Dependency for Tree-LSTM Aspect Sentiment Triplet Extraction (TASTE)](https://mlsanity.com/paper/2103.05815/) (2021)

## Built on by (most cited first, 2 of 2)

- [Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss](https://mlsanity.com/paper/2406.15175/) (2024)
- [Quantifying Compositionality of Classic and State-of-the-Art Embeddings](https://mlsanity.com/paper/2509.19332/) (2025)

## Abstract (arXiv)

When natural language phrases are combined, their meaning is often more than the sum of their parts. In the context of NLP tasks such as sentiment analysis, where the meaning of a phrase is its sentiment, that still applies. Many NLP studies on sentiment analysis, however, focus on the fact that sentiment computations are largely compositional. We, instead, set out to obtain non-compositionality ratings for phrases with respect to their sentiment. Our contributions are as follows: a) a methodology for obtaining those non-compositionality ratings, b) a resource of ratings for 259 phrases -- NonCompSST -- along with an analysis of that resource, and c) an evaluation of computational models for sentiment analysis using this new resource.