Affection Driven Neural Networks for Sentiment Analysis

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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)
Place of PublicationMarseille, France
PublisherThe European Language Resources Association(ELRA)
Number of pages8
ISBN (Print)979-10-95546-34-4
Publication statusPublished - 11 May 2020
Eventthe 12th Conference on Language Resources and Evaluation - Palais Du Pharo, Marseille, France
Duration: 11 May 202016 May 2020


Conferencethe 12th Conference on Language Resources and Evaluation
Abbreviated titleLREC 2020


  • Sentiment analysis
  • affective knowledge
  • attention mechanism


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