# Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets

arXiv: https://arxiv.org/abs/1709.04219 · HTML: https://mlsanity.com/paper/1709.04219/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2017.

## Built on by (most cited first, 9 of 9)

- [Decoding EEG Brain Activity for Multi-Modal Natural Language Processing](https://mlsanity.com/paper/2102.08655/) (2021)
- [Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language](https://mlsanity.com/paper/2109.10255/) (2021)
- [Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches](https://mlsanity.com/paper/2309.02092/) (2023)
- [The interplay between language similarity and script on a novel multi-layer Algerian dialect corpus](https://mlsanity.com/paper/2105.07400/) (2021)
- [Uzbek Sentiment Analysis based on local Restaurant Reviews](https://mlsanity.com/paper/2205.15930/) (2022)
- [ReDDIT: Regret Detection and Domain Identification from Text](https://mlsanity.com/paper/2212.07549/) (2022)
- [Emotional intelligence in large language models is fragmented across perception, cognition, and interaction](https://mlsanity.com/paper/2605.24686/) (2026)
- [Measuring Linguistic Diversity During COVID-19](https://mlsanity.com/paper/2104.01290/) (2021)
- [Visualizing Public Opinion on X: A Real-Time Sentiment Dashboard Using VADER and DistilBERT](https://mlsanity.com/paper/2504.15448/) (2025)

## Abstract (arXiv)

There has been a good amount of progress in sentiment analysis over the past 10 years, including the proposal of new methods and the creation of benchmark datasets. In some papers, however, there is a tendency to compare models only on one or two datasets, either because of time restraints or because the model is tailored to a specific task. Accordingly, it is hard to understand how well a certain model generalizes across different tasks and datasets. In this paper, we contribute to this situation by comparing several models on six different benchmarks, which belong to different domains and additionally have different levels of granularity (binary, 3-class, 4-class and 5-class). We show that Bi-LSTMs perform well across datasets and that both LSTMs and Bi-LSTMs are particularly good at fine-grained sentiment tasks (i. e., with more than two classes). Incorporating sentiment information into word embeddings during training gives good results for datasets that are lexically similar to the training data. With our experiments, we contribute to a better understanding of the performance of different model architectures on different data sets. Consequently, we detect novel state-of-the-art results on the SenTube datasets.