Leveraging Cognitive Features for Sentiment Analysis
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.
Cite
@article{arxiv.1701.05581,
title = {Leveraging Cognitive Features for Sentiment Analysis},
author = {Abhijit Mishra and Diptesh Kanojia and Seema Nagar and Kuntal Dey and Pushpak Bhattacharyya},
journal= {arXiv preprint arXiv:1701.05581},
year = {2017}
}
Comments
The SIGNLL Conference on Computational Natural Language Learning (CoNLL 2016)