Affection Driven Neural Networks for Sentiment Analysis

Rong Xiang, Yunfei Long, Mingyu Wan, Jinghang Gu, Qin Lu, Chu-Ren Huang


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.
Anthology ID:
2020.lrec-1.14
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
112–119
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.14
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.14.pdf