How Intense Are You? Predicting Intensities of Emotions and Sentiments using Stacked Ensemble [Application Notes]
Open accessAbstract
Emotions and sentiments are subjective in nature. They differ on a case-to-case basis. However, predicting only the emotion and sentiment does not always convey complete information. The degree or level of emotions and sentiments often plays a crucial role in understanding the exact feeling within a single class (e.g., `good' versus `awesome'). In this paper, we propose a stacked ensemble method for predicting the degree of intensity for emotion and sentiment by combining the outputs obtained from several deep learning and classical feature-based models using a multi-layer perceptron network. We develop three deep learning models based on convolutional neural network, long short-term memory and gated recurrent unit and one classical supervised model based on support vector regression. We evaluate our proposed technique for two problems, i.e., emotion analysis in the generic domain and sentiment analysis in the financial domain. The proposed model shows impressive results for both the problems. Comparisons show that our proposed model achieves improved performance over the existing state-of-the-art systems.
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The AFINN lexicon is one of the most used lexicons for sentiment analysis.
“I employ the AFINN lexicon developed by Finn Årup Nielsen (Nielsen, 2011) one of the most used lexicons for sentiment analysis (Akhtar et al., 2020;Chakraborty et al., 2020;Hee et al., 2018;Ragini et al., 2018)”
From The fingerprints of misinformation: how deceptive content differs from reliable sources in terms of cognitive effort and appeal to emotions (2022) · cites this paper as Akhtar et al., 2020
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466 · see citing works
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325 · see citing works
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831 · see citing works
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,482 · see citing works
An argument for basic emotions
Ekman, P. (1992) · Cognition and Emotion · doi:10.1080/02699939208411068 · Cited by 9,646 · see citing works
Experiments with a new boosting algorithm
Freund, Y., & Schapire, R. E. (1996) · Cited by 8,189 · see citing works
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches
Cho, K., van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014) · SSST@EMNLP · doi:10.3115/v1/w14-4012 · Cited by 7,251 · see citing works
VADER: a parsimonious rule-based model for sentiment analysis of social media text
Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087 · see citing works
Kittler, J., Hatef, M., Duin, R. P. W., & Matas, J. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.667881 · Cited by 5,312 · see citing works
Handbook of Affective Sciences
(2002) · doi:10.1093/oso/9780195126013.001.0001 · Cited by 5,104 · see citing works
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · see citing works
Russell, J. A., & Barrett, L. F. (1999) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.76.5.805 · Cited by 3,121 · see citing works
CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON
Mohammad, S. M., & Turney, P. D. (2012) · Computational Intelligence · doi:10.1111/j.1467-8640.2012.00460.x · Cited by 2,757 · see citing works
SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining
Baccianella, S., Esuli, A., & Sebastiani, F. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2jktnrykui9s · Cited by 2,691 · see citing works
A review of affective computing: From unimodal analysis to multimodal fusion
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.003 · Cited by 1,644 · see citing works
Self-citationAffective Computing and Sentiment Analysis
Cambria, E. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.31 · Cited by 1,445 · see citing works
Self-citationA holistic lexicon-based approach to opinion mining
Ding, X., Liu, B., & Yu, P. S. (2008) · doi:10.1145/1341531.1341561 · Cited by 1,398 · see citing works
WordNet Affect: an Affective Extension of WordNet
Strapparava, C., & Valitutti, A. (2004) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2yctm9q26ar4 · Cited by 1,387 · see citing works
NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of TweetsPreprint
Mohammad, S. M., Kiritchenko, S., & Zhu, X. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1308.6242 · Cited by 812 · see citing works
Textual analysis of stock market prediction using breaking financial news
Schumaker, R. P., & Chen, H. (2009) · ACM Transactions on Information Systems · doi:10.1145/1462198.1462204 · Cited by 778 · see citing works
A new ANEW: Evaluation of a word list for sentiment analysis in microblogsPreprint
Nielsen, F. Å. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.2903 · Cited by 733 · see citing works
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM
Ma, Y., Peng, H., & Cambria, E. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12048 · Cited by 606 · see citing works
Self-citationSentiment Analysis Is a Big Suitcase
Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2017) · IEEE Intelligent Systems · doi:10.1109/mis.2017.4531228 · Cited by 439 · see citing works
Self-citationNatural language based financial forecasting: a survey
Xing, F., Cambria, E., & Welsch, R. E. (2017) · Artificial Intelligence Review · doi:10.1007/s10462-017-9588-9 · Cited by 402 · see citing works
Self-citationMohammad, S. M. (2012) · Joint Conference on Lexical and Computational Semantics · Cited by 356 · see citing works
Wiebe, J., & Mihalcea, R. F. (2006) · International Conference on Computational Linguistics · doi:10.3115/1220175.1220309 · Cited by 298 · see citing works
WASSA-2017 Shared Task on Emotion Intensity
Mohammad, S. M., & Bravo-Márquez, F. (2017) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w17-5205 · Cited by 284 · see citing works
Sentiment Polarity Identification in Financial News: A Cohesion-based Approach
Devitt, A., & Ahmad, K. (2007) · Trinity's Access to Research Output (TARA) (Trinity College Dublin) · Cited by 275 · see citing works
Emotion recognition from text using semantic labels and separable mixture models
Wu, C., Chuang, Z.-J., & Lin, Y.-C. (2006) · ACM Transactions on Asian Language Information Processing · doi:10.1145/1165255.1165259 · Cited by 244 · see citing works
Exploiting Topic based Twitter Sentiment for Stock Prediction
Si, J., Mukherjee, A., Liu, B., Li, Q., Li, H., & Deng, X. (2013) · doi:10.13140/2.1.3604.7043 · Cited by 210 · see citing works
Baldwin, T., de Marneffe, M.-C., Han, B., Kim, Y.-B., Ritter, A., & Xu, W. H. (2015) · Workshop on Noisy User-generated Text (W-NUT) · doi:10.18653/v1/w15-4319 · Cited by 189 · see citing works
Feature selection and ensemble construction: A two-step method for aspect based sentiment analysis
Akhtar, M. S., Gupta, D. K., Ekbal, A., & Bhattacharyya, P. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.03.020 · Cited by 188 · see citing works
Self-citationEmotion Classification Using Web Blog Corpora
Yang, C., Lin, K., & Chen, H. (2007) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1109/wi.2007.51 · Cited by 163 · see citing works
SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
Cortis, K., Freitas, A., Daudert, T., Huerlimann, M., Zarrouk, M., Handschuh, S., & Davis, B. (2017) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s17-2089 · Cited by 159 · see citing works
Fine-grained analysis of explicit and implicit sentiment in financial news articles
de Kauter, M. V., Breesch, D., & Hoste, V. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.02.007 · Cited by 136 · see citing works
Intelligent Asset Allocation via Market Sentiment Views
Xing, F., Cambria, E., & Welsch, R. E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2866727 · Cited by 130 · see citing works
Self-citationAll-in-One: Emotion, Sentiment and Intensity Prediction Using a Multi-Task Ensemble Framework
Akhtar, M. S., Ghosal, D., Ekbal, A., Bhattacharyya, P., & Kurohashi, S. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2926724 · Cited by 122 · see citing works
Self-citationOn the Predictability of Stock Market Behavior Using StockTwits Sentiment and Posting Volume
Oliveira, N., Cortez, P. C., & Areal, N. M. D. P. B. D. C. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40669-0_31 · Cited by 121 · see citing works
Sentic Computing for patient centered applications
Cambria, E., Hussain, A., Durrani, T. S., Havasi, C., Eckl, C., & Munro, J. (2010) · International Conference on Signal Processing · doi:10.1109/icosp.2010.5657072 · Cited by 119 · see citing works
Self-citationTopic-dependent sentiment analysis of financial blogs
O’Hare, N., Davy, M., Bermingham, A., Ferguson, P., Sheridan, P., Gurrin, C. G., & Smeaton, A. F. (2009) · doi:10.1145/1651461.1651464 · Cited by 108 · see citing works
Best-worst scaling: theory and methods
Flynn, T. N., & Marley, A. A. J. (2014) · Edward Elgar Publishing eBooks · doi:10.4337/9781781003152.00014 · Cited by 101 · see citing works
A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis
Akhtar, M. S., Kumar, A., Ghosal, D., Ekbal, A., & Bhattacharyya, P. (2017) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1057 · Cited by 95 · see citing works
Self-citationCambria, E., Rajagopal, D., Olsher, D. J., & Das, D. (2013) · Chapman & Hall/CRC eBooks · doi:10.1201/b16014-19 · Cited by 82 · see citing works
Self-citationLearning binary codes with neural collaborative filtering for efficient recommendation systems
Li, Y., Wang, S., Pan, Q., Peng, H., Yang, T., & Cambria, E. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.02.012 · Cited by 81 · see citing works
Self-citationDetermining Word-Emotion Associations from Tweets by Multi-label Classification
Bravo-Márquez, F., Frank, E., Mohammad, S. M., & Pfahringer, B. (2016) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1109/wi.2016.0091 · Cited by 80 · see citing works
Goel, P., Kulshreshtha, D., Jain, P., & Shukla, K. K. (2017) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w17-5207 · Cited by 63 · see citing works
Weighted Vote-Based Classifier Ensemble for Named Entity Recognition
Ekbal, A., & Saha, S. (2011) · ACM Transactions on Asian Language Information Processing · doi:10.1145/1967293.1967296 · Cited by 61 · see citing works
Self-citationCambria, E., Rajagopal, D., Olsher, D. J., & Das, D. (2013) · Chapman & Hall/CRC eBooks · doi:10.1201/b16014-24 · Cited by 61 · see citing works
Self-citationKöper, M., Kim, E., & Klinger, R. (2017) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w17-5206 · Cited by 53 · see citing works
Bagging and Boosting statistical machine translation systems
Xiao, T., Zhu, J., & Liu, T. (2012) · Artificial Intelligence · doi:10.1016/j.artint.2012.11.005 · Cited by 46 · see citing works
A High-Order Hidden Markov Model for Emotion Detection from Textual Data
Ho, D. T., & Cao, T. H. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-32541-0_8 · Cited by 43 · see citing works
Jiang, M., Lan, M., & Wu, Y. (2017) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s17-2152 · Cited by 37 · see citing works
Using frame-based resources for sentiment analysis within the financial domain
Atzeni, M., Dridi, A., & Recupero, D. R. (2018) · Progress in Artificial Intelligence · doi:10.1007/s13748-018-0162-8 · Cited by 36 · see citing works
Mansar, Y., Gatti, L., Ferradans, S., Guerini, M., & Staiano, J. (2017) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s17-2138 · Cited by 34 · see citing works
IITP: Hybrid Approach for Text Normalization in Twitter
Akhtar, M. S., Sikdar, U. K., & Ekbal, A. (2015) · Workshop on Noisy User-generated Text (W-NUT) · doi:10.18653/v1/w15-4316 · Cited by 23 · see citing works
Self-citationRecognition of Emotion with SVMs
Teng, Z., Ren, F., & Kuroiwa, S. (2006) · Lecture notes in computer science · doi:10.1007/11816171_87 · Cited by 19 · see citing works
A Novel Attention Based CNN Model for Emotion Intensity Prediction
Xie, H., Feng, S., Wang, D., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99495-6_31 · Cited by 17 · see citing works
An Ensemble Based Method for Predicting Emotion Intensity of Tweets
Madisetty, S., & Desarkar, M. S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71928-3_34 · Cited by 16 · see citing works
Akhtar, M. S., Sawant, P., Ekbal, A., Pawar, J. D., & Bhattacharyya, P. (2017) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w17-5229 · Cited by 8 · see citing works
Self-citationGhosal, D., Akhtar, M. S., Ekbal, A., & Bhattacharyya, P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04221-9_15 · Cited by 8 · see citing works
Self-citation
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Research works printed but not linked: 3
- J. Wiebe and R. Mihalcea, "Word sense and subjectiv- ity," in Proc. Int. Conf. Computational Linguistics/ Association of Computational Linguistics, Sydney, Australia, 2006, pp. 1065-1072.
- S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python: Analyzing Text with the Natural Lan- guage Toolkit. O'Reilly Media, 2009.
- S. Khosla, N. Chhaya, and K. Chawla, "Aff2Vec: Affect-enriched distributional word representations," in Proc. 27th Int. Conf. Computational Linguistics, Santa Fe, NM, 2018, pp. 2204-2218.
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- Authors
- Akhtar, M. S., Ekbal, A., & Cambria, E.
- Affiliations
- Indian Institute of Technology Patna (IN)
- Nanyang Technological University (SG)
- Venue
- IEEE Computational Intelligence Magazine
- Published
- 2020-02
- Volume
- 15
- Issue
- 1
- Pages
- 64-75
- ISSN
- 1556-603X (ISSN-L)
- 1556-6048
- OpenAlex
- W3000271234
- Cited by
- 246
- References
- 65
- Citations by year
- 2026: 6 · 2025: 20 · 2024: 22 · 2023: 27 · 2022: 46 · 2021: 101 · 2020: 24
- FWCI
- 19.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- IEEE Computational Intelligence Magazine (publishedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Funders
- Ministry of Electronics and Information technology
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- emotion intensity prediction, stacking ensemble, multilayer perceptron, convolutional neural networks, long short-term memory, gated recurrent unit, support vector regression, financial sentiment analysis, emotion analysis
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