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Affection Driven Neural Networks for Sentiment Analysis

Xiang, Rong and Long, Yunfei and Wan, Mingyu and Gu, Jinghang and Lu, Qin and Huang, Chu-Ren (2020) Affection Driven Neural Networks for Sentiment Analysis. In: 12th Language Resources and Evaluation Conference, 2020-05-11 - 2020-05-16, Marseille, France.

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Abstract

Deep neural network models have played a critical role in sentiment analysis with promising results in the recent decade. One of the essential challenges, however, is how external sentiment knowledge can be effectively utilized. In this work, we propose a novel affection-driven approach to incorporating affective knowledge into neural network models. The affective knowledge is obtained in the form of a lexicon under the Affect Control Theory (ACT), which is represented by vectors of three-dimensional attributes in Evaluation, Potency, and Activity (EPA). The EPA vectors are mapped to an affective influence value and then integrated into Long Short-term Memory (LSTM) models to highlight affective terms. Experimental results show a consistent improvement of our approach over conventional LSTM models by 1.0% to 1.5% in accuracy on three large benchmark datasets. Evaluations across a variety of algorithms have also proven the effectiveness of leveraging affective terms for deep model enhancement.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Proceedings of the 12th Language Resources and Evaluation Conference
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 03 Dec 2020 10:37
Last Modified: 03 Dec 2020 10:37
URI: http://repository.essex.ac.uk/id/eprint/29277

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