Academic Journal of Computing & Information Science, 2020, 3(3); doi: 10.25236/AJCIS.2020.030314.
Beijing No.4 High School International Campus, Beijing, China
*Corresponding author e-mail: firstname.lastname@example.org
Emotion detection and opinion mining in social network is a hot research topic nowadays. Most existing studies and research, however, have been using typical binary (positive/negative) or ternary (positive / negative / neutral) classification. Classifying short sequences of text into multi subclasses is relatively rarely reported. Except Bouazizi and Ohtsuki (2017), the accuracy rate of detection in their two studies was only 56.9% and 60.2%. In our study, we proposed a Bi-directional Long Short-Term Memory with Language Model (BiLSTM-LM) to classify text sequences into seven distinct emotional classes. Results showed that the accuracy rate of detection based on our model can reach as high as 64.09% on multi-class classification, which is 4 percentage points higher than the most advanced model in the world to date.
Emotion detection, opinion mining, LSTM
Zihe Wang. Text Emotion Detection Based on Bi- LSTM Network. Academic Journal of Computing & Information Science (2020), Vol. 3, Issue 3: 129-137. https://doi.org/10.25236/AJCIS.2020.030314.
 J. Cahn, “Generating Expression in Synthesized Speech,” Master’s thesis, MIT, 1989.
 Keikichi Hirose, Nobuaki Minematsu, etc, “Analytical and perceptual study on the role of acoustic features in realizing emotional speech”, ICSLP2000.
 Donna Erickson, 1 Arthur Abramson, “Articulatory characteristics of emotional utterances in spoken English”, ICSLP2000
 Schröder M1, Cowie R2, Douglas-Cowie E2, “Acoustic Correlates of Emotion Dimensions in View of Speech Synthesis”, Eurospeech2001
 K. Nurzynska and B. Smolka, "Recognition between smiling and neutral facial display with power LBP operator," IEEE EUROCON 2015 - International Conference on Computer as a Tool (EUROCON), Salamanca, 2015, pp. 1-6.
 T. Kiran and T. Kushal, "Facial expression classification using Support Vector Machine based on bidirectional Local Binary Pattern Histogram feature descriptor," 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Shanghai, 2016, pp. 115-120.
 C. Cheng, X. Wei and Z. Jian, "Emotion recognition algorithm based on convolution neural network," 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Nanjing, 2017, pp. 1-5.
 S. Basu, J. Chakraborty and M. Aftabuddin, "Emotion recognition from speech using convolutional neural network with recurrent neural network architecture," 2017 2nd International Conference on Communication and Electronics Systems (ICCES), Coimbatore, 2017, pp. 333-336.
 Ze-Jing Chuang and Chung-Hsien Wu, “Emotion recognition from textual input using an emotional semantic network”, ICSLP2002, Denver.
 Feng Xiang, Qiu Longhui, Guo Xiaoran. A Method of Student Feedback Text Recognition Based on LSTM Model [J]. Open Education Research, 2019, 25 (02): 114-120.
 Taboada M, Brooke J, Tofiloski M, et al. Lexicon-Based Methods for Sentiment Analysis [J]. Computational Linguistics, 2011, 37 (2): 267-307.
 Cheng Bo, Li Weihong, Tong Tong. Chinese hierarchical address segmentation based on BiLSTM-CRF [J/OL]. Journal of Geo-Information Science, 2019 (08): 1-9.