# Leveraging Cognitive Features for Sentiment Analysis

arXiv: https://arxiv.org/abs/1701.05581 · HTML: https://mlsanity.com/paper/1701.05581/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2017.

## Built on by (most cited first, 19 of 19)

- [Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze?](https://mlsanity.com/paper/2205.10226/) (2022)
- [Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading](https://mlsanity.com/paper/2304.10784/) (2023)
- [A Survey on Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2203.14266/) (2022)
- [Synthesizing Human Gaze Feedback for Improved NLP Performance](https://mlsanity.com/paper/2302.05721/) (2023)
- [Decoding EEG Brain Activity for Multi-Modal Natural Language Processing](https://mlsanity.com/paper/2102.08655/) (2021)
- [Seeing Eye to AI: Human Alignment via Gaze-Based Response Rewards for Large Language Models](https://mlsanity.com/paper/2410.01532/) (2024)
- [CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals](https://mlsanity.com/paper/2106.05544/) (2021)
- [A Survey on Using Gaze Behaviour for Natural Language Processing](https://mlsanity.com/paper/2112.15471/) (2021)
- [The Copenhagen Corpus of Eye Tracking Recordings from Natural Reading of Danish Texts](https://mlsanity.com/paper/2204.13311/) (2022)
- [WebQAmGaze: A Multilingual Webcam Eye-Tracking-While-Reading Dataset](https://mlsanity.com/paper/2303.17876/) (2023)
- [PoTeC: A German Naturalistic Eye-tracking-while-reading Corpus](https://mlsanity.com/paper/2403.00506/) (2024)
- [EMTeC: A Corpus of Eye Movements on Machine-Generated Texts](https://mlsanity.com/paper/2408.04289/) (2024)
- [OASST-ETC Dataset: Alignment Signals from Eye-tracking Analysis of LLM Responses](https://mlsanity.com/paper/2503.10927/) (2025)
- [DocTrack: A Visually-Rich Document Dataset Really Aligned with Human Eye Movement for Machine Reading](https://mlsanity.com/paper/2310.14802/) (2023)
- [Does Attention Mechanism Possess the Feature of Human Reading? A Perspective of Sentiment Classification Task](https://mlsanity.com/paper/2209.03557/) (2022)
- [Near-Zero-Shot Suggestion Mining with a Little Help from WordNet](https://mlsanity.com/paper/2111.12956/) (2021)
- [Decoding Open-Ended Information Seeking Goals from Eye Movements in Reading](https://mlsanity.com/paper/2505.02872/) (2025)
- [Eyettention II: A Dual-Sequence Architecture for Modeling Fixation Location, Within-Word Landing Position, and Fixation Duration in Reading](https://mlsanity.com/paper/2606.01964/) (2026)
- [Leveraging Human Reading Behavior for Keyphrase Extraction: A Webcam-based Eye-tracking Corpus](https://mlsanity.com/paper/2608.10688/) (2026)

## Abstract (arXiv)

Sentiments expressed in user-generated short text and sentences are nuanced by subtleties at lexical, syntactic, semantic and pragmatic levels. To address this, we propose to augment traditional features used for sentiment analysis and sarcasm detection, with cognitive features derived from the eye-movement patterns of readers. Statistical classification using our enhanced feature set improves the performance (F-score) of polarity detection by a maximum of 3.7% and 9.3% on two datasets, over the systems that use only traditional features. We perform feature significance analysis, and experiment on a held-out dataset, showing that cognitive features indeed empower sentiment analyzers to handle complex constructs.