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.

Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network

“…Task Table 1 presents results for sentiment analysis task. For dataset 1, different variants of our CNN architecture outperform the best systems reported by Mishra et al (2016c), with a maximum F-score improvement of 3.8%. This improvement is statistically significant of p < 0.05 as confirmed by McNemar test.…”

“…For sentiment analysis, we compare our systems's accuracy (for both datasets 1 and 2) with Mishra et al (2016c)'s systems that rely on handcrafted text and gaze features. For sarcasm detection, we compare Mishra et al (2016b)'s sarcasm classifier with ours using dataset 1 (with available gold standard labels for sarcasm).…”

“…Klerke et al (2016) present a novel multi-task learning approach for sentence compression using labeled data, while, Barrett and Søgaard (2015) discriminate between grammatical functions using gaze features. The closest works to ours are by Mishra et al (2016b) and Mishra et al (2016c) that introduce feature engineering based on both gaze and text data for sentiment and sarcasm detection tasks. These recent advancements motivate us to explore the cognitive NLP paradigm.…”

A Cognition Based Attention Model for Sentiment Analysis

“…Mishra (2014) presents a cognitive study of sentiment detection from the perspective of AI where readers are tested as sentiment readers. Mishra (Mishra et al, 2016b) recently proposes a model in sentiment analysis and sarcasm detection by using eye-tracking data as a feature in addition to text features using Naive-Bayes and SVM classifiers.…”

“…Among different available eye-tracking datasets, the Dundee corpus, GECO (the Ghent Eye-Tracking Corpus), and Mishra et al (Mishra et al, 2016b) are considered high-quality resources (Kennedy, 2003;Cop et al, 2016;Mishra et al, 2016b). The Dundee corpus contains eye movement data from English and French newspapers (Kennedy, 2003).…”