Incremental Encoding Transformer Incorporating Common-sense Awareness for Conversational Sentiment Recognition
Authors: 
Xiao Yang, Xiaopeng Cao, Hao LiangAuthors Info & Claims
Xiao Yang, Xiaopeng Cao, Hao LiangAuthors Info & ClaimsPages 148 - 153
Abstract
Conversational sentiment recognition has been widely used in people's lives and work. However, machines do not understand emotions through common-sense cognition. We propose an Incremental Encoding Transformer Incorporating Common-sense Awareness (IETCA) model. The model helps the machines use common-sense knowledge to better understand emotions in conversation. The model uses a context-aware graph attention mechanism to obtain knowledge-rich utterance representations and uses an incremental encoding Transformer to get rich contextual representations. We do some experiments on five datasets. The results show that the model has some improvement in conversational sentiment recognition.
References
[1]
Dahai Guo, "Tracking Student Sentiment from Social Media," Vol. 6, No. 2, pp. 80-83, May, 2015.
[2]
H K Darshan, Aditya R Shankar, B S Harish, and Keerthi Kumar H M, "Exploiting RLPI for Sentiment Analysis on Movie Reviews," Journal of Advances in Information Technology, Vol. 10, No. 1, pp. 14-19, February 2019.
[3]
Sirisha Velampalli, Chandrashekar Muniyappa, and Ashutosh Saxena, "Performance Evaluation of Sentiment Analysis on Text and Emoji Data Using End-to-End, Transfer Learning, Distributed and Explainable AI Models," Journal of Advances in Information Technology, Vol. 13, No. 2, pp. 167-172, April 2022.
[4]
Majumder N, Poria S, Hazarika D, DialogueRNN: An Attentive RNN for Emotion Detection in Conversations[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33: 6818-6825.
[5]
Zhang D, Wu L, Sun C, Modeling both Context- and Speaker-Sensitive Dependence for Emotion Detection in Multi-speaker Conversations[C]. Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}, 2019.
[6]
Cheng J, Dong L, Lapata M. Long Short-Term Memory-Networks for Machine Reading[J], 2016.
[7]
Bahdanau D, Cho K, Bengio Y. Neural Machine Translation by Jointly Learning to Align and Translate[J]. Computer Science, 2014.
[8]
Devlin J, Chang M W, Lee K, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[J], 2018.
[9]
Xiaoyi Zhao and Yukio Ohsawa, "Sentiment Analysis on the Online Reviews Based on Hidden Markov Model," Vol. 9, No. 2, pp. 33-38, May 2018.
[10]
Soujanya Poria, Erik Cambria, Devamanyu Hazarika, Navonil Majumder, Amir Zadeh, and Louis-Philippe Morency. 2017. Context-dependent sentiment analysis in user-generated videos. In ACL, volume 1, pages 873–883.
[11]
Chen S Y, Hsu C C, Kuo C C, EmotionLines: An Emotion Corpus of Multi-Party Conversations[J], 2018.
[12]
Cerisara C, Jafaritazehjani S, Oluokun A, Multi-task dialog act and sentiment recognition on Mastodon[J], 2018.
[13]
WANG J C, XU Y, LIU Q Y, Dialog sentiment analysis with neural topic model[J]. Journal of Chinese Information Processing, 2020, 34(1): 106-112.
[14]
Alaa Hamouda, Mahmoud Marei, and Mohamed Rohaim, "Building Machine Learning Based Senti-word Lexicon for Sentiment Analysis," Journal of Advances in Information Technology, Vol. 2, No. 4, pp. 199-203, November, 2011.
[15]
Mohammad Darwich, Shahrul Azman Mohd Noah, Nazlia Omar, Nurul Aida Osman, and Ibrahim Said Ahmad, "Quantifying the Natural Sentiment Strength of Polar Term Senses Using Semantic Gloss Information and Degree Adverbs," Journal of Advances in Information Technology, Vol. 11, No. 3, pp. 109-118, August 2020.
[16]
Ghosal D, Majumder N, Gelbukh A, COSMIC: COmmonSense knowledge for eMotion Identification in Conversations[J], 2020.
[17]
Speer R, Chin J, Havasi C. ConceptNet 5.5: An Open Multilingual Graph of General Knowledge[J], 2016.
[18]
Mohammad S. Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words[C]. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018.
[19]
Li S, Pan R, Luo H, Adaptive cross-contextual word embedding for word polysemy with unsupervised topic modeling[J]. Knowledge-Based Systems, 2021, 218(4):106827.
[20]
Ashish V aswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In NIPS, pages 5998–6008.
[21]
Zhong P, Wang D, Miao C. Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations[J], 2019.
[22]
Chatterjee A, Narahari K N, Joshi M, SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text[C]. Proceedings of the 13th International Workshop on Semantic Evaluation, 2019.
[23]
Li Y, Hui S, Shen X, DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset[J], 2017.
[24]
Poria S, Hazarika D, Majumder N, MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations[J], 2018.
[25]
Zahiri S M, Choi J D. Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks[J], 2017.
[26]
Busso C, Bulut M, Lee C C, IEMOCAP: interactive emotional dyadic motion capture database[J]. Language Resources and Evaluation, 2008, 42(4): 335-359.
[27]
Ac A, Ug A, Mkc B, Understanding Emotions in Text Using Deep Learning and Big Data[J]. Computers in Human Behavior, 2019, 93: 309-317.
[28]
Kim Y. Convolutional Neural Networks for Sentence Classification[J]. Eprint Arxiv, 2014.
Index Terms
- Incremental Encoding Transformer Incorporating Common-sense Awareness for Conversational Sentiment Recognition
Recommendations
Enhancing Conversational Aspect-Based Sentiment Quadruple Analysis with Context Fusion Encoding Method
Natural Language Processing and Chinese ComputingAbstractAspect-based sentiment analysis (ABSA) has been a hot research topic due to its ability to fully exploit people’s opinions through social media texts. Compared with analyzing sentiment in short texts, conversational aspect-based sentiment ...
Incorporating Sentiment Prior Knowledge for Weakly Supervised Sentiment Analysis
This article presents two novel approaches for incorporating sentiment prior knowledge into the topic model for weakly supervised sentiment analysis where sentiment labels are considered as topics. One is by modifying the Dirichlet prior for topic-word ...
A novel span-based Knowledge-enhanced framework for aspect sentiment triplet extraction
AbstractAspect Sentiment Triplet Extraction (ASTE) is a subtask of Aspect-based Sentiment Analysis, which aims to identify all aspect sentiment triplets in given sentences. ASTE is an important research task to discover sentimental opinions in online ...
Highlights- SKen incorporates common-sense knowledge to enhance the model’s representation.
- SKen introduces orthogonal attention loss to focus more on sentiment elements.
- SKen demonstrates its superiority when compared with large language ...
Comments
Information & Contributors
Information
Published In
September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 May 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Key Research and Development Program of Shaanxi Province
Conference
AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 21Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)2
Reflects downloads up to 01 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format