Next Article in Journal
Quadrupedal Robots Whole-Body Motion Control Based on Centroidal Momentum Dynamics
Next Article in Special Issue
Classification of Cyber-Aggression Cases Applying Machine Learning
Previous Article in Journal
Improving Eight-State Continuous Variable Quantum Key Distribution by Applying Photon Subtraction
Previous Article in Special Issue
Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum
Article

Sentiment-Aware Word Embedding for Emotion Classification

by 1,†,‡, 2,‡, 2,*, 2,* and 2
1
Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
2
School of Computer Science and Engineering, Central South University, Changsha 410073, China
*
Authors to whom correspondence should be addressed.
Current address: National University of Defense Technology, Changsha 410073, China.
These authors contributed equally to this work.
Appl. Sci. 2019, 9(7), 1334; https://doi.org/10.3390/app9071334
Received: 12 February 2019 / Revised: 19 March 2019 / Accepted: 22 March 2019 / Published: 29 March 2019
(This article belongs to the Special Issue Sentiment Analysis for Social Media)
Word embeddings are effective intermediate representations for capturing semantic regularities between words in natural language processing (NLP) tasks. We propose sentiment-aware word embedding for emotional classification, which consists of integrating sentiment evidence within the emotional embedding component of a term vector. We take advantage of the multiple types of emotional knowledge, just as the existing emotional lexicon, to build emotional word vectors to represent emotional information. Then the emotional word vector is combined with the traditional word embedding to construct the hybrid representation, which contains semantic and emotional information as the inputs of the emotion classification experiments. Our method maintains the interpretability of word embeddings, and leverages external emotional information in addition to input text sequences. Extensive results on several machine learning models show that the proposed methods can improve the accuracy of emotion classification tasks. View Full-Text
Keywords: emotion classification; sentiment lexicon; text feature representation; hybrid vectorization; sentiment-aware word embedding emotion classification; sentiment lexicon; text feature representation; hybrid vectorization; sentiment-aware word embedding
Show Figures

Figure 1

MDPI and ACS Style

Mao, X.; Chang, S.; Shi, J.; Li, F.; Shi, R. Sentiment-Aware Word Embedding for Emotion Classification. Appl. Sci. 2019, 9, 1334. https://doi.org/10.3390/app9071334

AMA Style

Mao X, Chang S, Shi J, Li F, Shi R. Sentiment-Aware Word Embedding for Emotion Classification. Applied Sciences. 2019; 9(7):1334. https://doi.org/10.3390/app9071334

Chicago/Turabian Style

Mao, Xingliang; Chang, Shuai; Shi, Jinjing; Li, Fangfang; Shi, Ronghua. 2019. "Sentiment-Aware Word Embedding for Emotion Classification" Appl. Sci. 9, no. 7: 1334. https://doi.org/10.3390/app9071334

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop