# Leveraging Cognitive Features for Sentiment Analysis

## TL;DR

This paper introduces cognitive features based on eye-movement patterns to enhance sentiment analysis, significantly improving polarity detection accuracy on multiple datasets.

## Contribution

It presents a novel approach of incorporating eye-tracking derived cognitive features into sentiment analysis models, boosting their ability to handle nuanced and complex sentiments.

## Key findings

- Enhanced F-score by up to 3.7% and 9.3% on two datasets
- Cognitive features significantly improve sentiment detection accuracy
- Feature significance analysis confirms the value of eye-movement data

## Abstract

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.