Abstract
Emotions detection in natural languages is very effective in analyzing the user's mood about a concerned product, news, topic, and so on. However, it is really a challenging task to extract important features from a burst of raw social text, as emotions are subjective with limited fuzzy boundaries. These subjective features can be conveyed in various perceptions and terminologies. In this article, we proposed an IoT-based framework for emotions classification of tweets using a hybrid approach of Term Frequency Inverse Document Frequency (TFIDF) and deep learning model. First, the raw tweets are filtered using the tokenization method for capturing useful features without noisy information. Second, the TFIDF statistical technique is applied to estimate the importance of features locally as well as globally. Third, the Adaptive Synthetic (ADASYN) class balancing technique is applied to solve the imbalance class issue among different classes of emotions. Finally, a deep learning model is designed to predict the emotions with dynamic epoch curves. The proposed methodology is analyzed on two different Twitter emotions datasets. The dynamic epoch curves are shown to show the behavior of test and train data points. It is proved that this methodology outperformed the popular state-of-the-art methods.
- H. Htet, S. S. Khaing, and Y. Y. Myint. 2018. Tweets sentiment analysis for healthcare on big data processing and IoT architecture using maximum entropy classifier. In Proceedings of the International Conference on Big Data Analysis and Deep Learning Applications. Springer, 2018.Google Scholar
- I. Khan, S. Naqvi, M. Alam, and S. Rizvi. 2017. An efficient framework for real-time tweet classification. Int. J. Inf. Technol. 9, 2 (2017), 215–221.Google Scholar
Cross Ref
- S. M. Alzahrani. 2018. Development of IoT mining machine for Twitter sentiment analysis: Mining in the cloud and results on the mirror. In 2018 15th Learning and Technology Conference (L&T). IEEE, 86--95.Google Scholar
Cross Ref
- E.-A. Baatarjav, S. Phithakkitnukoon, and R. Dantu. 2008. Group recommendation system for Facebook. In OTM Confederated International Conferences “On the Move to Meaningful Internet Systems”. Springer, Berlin, Heidelberg, 211--219. Google Scholar
Digital Library
- F. M. R. Pardo and A. P. Padilla. 2009. Detecting blogs independently from the language and content. In 1st International Workshop on Mining Social Media (MSM09-CAEPIA09). Citeseer.Google Scholar
- M. J. Peltola, L. Forssman, K. Puura, M. H. van IJzendoorn, and J. M. Leppänen. 2015. Attention to faces expressing negative emotion at 7 months predicts attachment security at 14 months. Child Dev. 86, 5 (2015), 1321–1332.Google Scholar
Cross Ref
- R. K. Sakthivel, G. Nagasubramanian, F. Al‐Turjman, and M. Sankayya. 2020. Core‐level cybersecurity assurance using cloud‐based adaptive machine learning techniques for manufacturing industry. Trans. Emerg. Telecommun. Technol. 15 (2020), e3947.Google Scholar
- J. Whitehill, Z. Serpell, Y.-C. Lin, A. Foster, and J. R. Movellan. 2014. The faces of engagement: Automatic recognition of student engagement from facial expressions. IEEE Trans. Affect. Comput. 5, 1 (2014), 86–98.Google Scholar
Cross Ref
- V. K. Neppalli, C. Caragea, A. Squicciarini, A. Tapia, and S. Stehle. 2017. Sentiment analysis during Hurricane Sandy in emergency response. Int. J. Disast. Risk Reduct. 21 (2017), 213–222.Google Scholar
Cross Ref
- X. Ji, S. A. Chun, Z. Wei, and J. Geller. 2015. Twitter sentiment classification for measuring public health concerns. Soc. Netw. Anal. Mining 5, 1 (2015), 13.Google Scholar
Cross Ref
- A. M. Kaplan and M. Haenlein. 2010. Users of the world, unite! The challenges and opportunities of social media. Bus. Horiz. 53, 1 (2010), 59–68.Google Scholar
Cross Ref
- N. Jamal, C. Xianqiao, and H. Aldabbas. 2019. Deep learning-based sentimental analysis for large-scale imbalanced Twitter data. Fut. Internet 11, 9 (2019), 190.Google Scholar
Cross Ref
- F. H. Khan, S. Bashir, and U. Qamar. 2014. TOM: Twitter opinion mining framework using hybrid classification scheme. Decis. Supp. Syst. 57 (2014), 245–257. Google Scholar
Digital Library
- S. Jacob, V. G. Menon, F. Al-Turjman, P. Vinoj, and L. Mostarda. 2019. Artificial muscle intelligence system with deep learning for post-stroke assistance and rehabilitation. IEEE Access 7 (2019), 133463–133473.Google Scholar
Cross Ref
- G. Bel-Enguix, H. Gómez-Adorno, J. Reyes-Magaña, and G. Sierra. 2019. Wan2Vec: Embeddings learned on word association norms. Semant. Web, Preprint 10, 6 (2019), 991–1006.Google Scholar
Cross Ref
- R. A. Stein, P. A. Jaques, and J. F. Valiati. 2019. An analysis of hierarchical text classification using word embeddings. Inf. Sci. 471 (2019), 216–232.Google Scholar
Cross Ref
- R. S. Olson and J. H. Moore. 2019. TPOT: A tree-based pipeline optimization tool for automating machine learning. In Workshop on Automatic Machine Learning. PMLR, 66--74.Google Scholar
- G. Beigi, X. Hu, R. Maciejewski, and H. Liu. 2016. An overview of sentiment analysis in social media and its applications in disaster relief. In Sentiment Analysis and Ontology Engineering. Springer, Cham, 313--340.Google Scholar
- H. Gunes, B. Schuller, M. Pantic, and R. Cowie. 2011. Emotion representation, analysis and synthesis in continuous space: A survey. In Face and Gesture. IEEE, 827--834.Google Scholar
- B. Felbo, A. Mislove, A. Søgaard, I. Rahwan, and S. Lehmann. 2017. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. Arxiv Preprint Arxiv:1708.00524 (2017).Google Scholar
- W. Wang, L. Chen, K. Thirunarayan, and A. P. Sheth. 2012. Harnessing Twitter “Big Data” for automatic emotion identification. In 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing. IEEE, 587--592. Google Scholar
Digital Library
- S. M. Mohammad and S. Kiritchenko. 2015. Using hashtags to capture fine emotion categories from tweets. Computat. Intell. 31, 2 (2015), 301–326. Google Scholar
Digital Library
- S. M. Mohammad, S. Kiritchenko, and X. Zhu. 2013. NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets. Arxiv Preprint Arxiv:1308.6242 (2013).Google Scholar
- P. Ekman. 1992. An argument for basic emotions. Cog. Emot. 6, 3--4 (1992), 169–200.Google Scholar
Cross Ref
- R. Plutchik. 1980. A General Psychoevolutionary Theory of Emotion, Emotion Research, Theory, and Experience (Theories of Emotion, Vol. 1), R. Plutchik and H. Kellerman (Eds.). Academic Press.Google Scholar
- J. C. Norcross, E. Guadagnoli, and J. O. Prochaska. 1984. Factor structure of the profile of mood states (POMS): Two partial replications. J. Clin. Psychol. 40, 5 (1984), 1270–1277.Google Scholar
Cross Ref
- P. Badjatiya, S. Gupta, M. Gupta, and V. Varma. 2017. Deep learning for hate speech detection in Tweets. In Proceedings of the 26th International Conference on World Wide Web Companion. 759--760. Google Scholar
Digital Library
- X. Yang, C. Macdonald, and I. Ounis. 2018. Using word embeddings in Twitter election classification. Inf. Retr. J. 21, 2--3 (2018), 183–207. Google Scholar
Digital Library
- J. Deriu, A. Lucchi, V. De Luca, A. Severyn, S. Müller, M. Cieliebak, T. Hofmann, and M. Jaggi. 2017. Leveraging large amounts of weakly supervised data for multi-language sentiment classification. In Proceedings of the 26th International Conference on World Wide Web. 1045--1052. Google Scholar
Digital Library
- A. Bifet and E. Frank. 2010. Sentiment knowledge discovery in Twitter streaming data. In International Conference on Discovery Science. Springer, Berlin, Heidelberg, 1--15. Google Scholar
Digital Library
- A. Summa, B. Resch, and M. Strube. 2016. Microblog emotion classification by computing similarity in text, time, and space. In Proceedings of the Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media (PEOPLES'16). 153--162.Google Scholar
- E. Ferrara and Z. Yang. 2015. Measuring emotional contagion in social media. PloS One 10, 11 (2015), e0142390.Google Scholar
Cross Ref
- J. Bollen, H. Mao, and A. Pepe. 2011. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. arXiv preprint arXiv:0911.1583 (2009).Google Scholar
- F. Colace, M. De Santo, and L. Greco. 2013. A probabilistic approach to tweets’ sentiment classification.Google Scholar
- H. H. Saeed, T. Calders, and F. Kamiran. 2020. OSACT4 shared tasks: Ensembled stacked classification for offensive and hate speech in Arabic tweets. In Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection. 71--75.Google Scholar
- A. Esuli, A. Moreo Fernández, and F. Sebastiani. 2018. A recurrent neural network for sentiment quantification. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 1775--1778. Google Scholar
Digital Library
- F. Ullah, J. Wang, S. Jabbar, F. Al-Turjman, and M. Alazab. 2019. Source code authorship attribution using hybrid approach of program dependence graph and deep learning model. IEEE Access 7 (2019), 141987–141999.Google Scholar
Cross Ref
- S. Lal, L. Tiwari, R. Ranjan, A. Verma, N. Sardana, and R. Mourya. 2020. Analysis and classification of crime tweets. Procedia Comput. Sci. 167 (2020), 1911–1919.Google Scholar
Cross Ref
- Y. Q. Lim, C. M. Lim, K. H. Gan, and N. H. Samsudin. 2020. Text sentiment analysis on Twitter to identify positive or negative context in addressing inept regulations on social media platform. In 2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics (ISCAIE'20). IEEE, 96--101.Google Scholar
- J. H. Paik. 2013. A novel TF-IDF weighting scheme for effective ranking. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. 343--352. Google Scholar
Digital Library
- F. Ullah, H. Naeem, S. Jabbar, S. Khalid, M. A. Latif. 2019. Cyber security threats detection in internet of things using deep learning approach. IEEE Access, 7 (2019), 124379–124389.Google Scholar
Cross Ref
- B. P. Eddy, J. A. Robinson, N. A. Kraft, and J. C. Carver. 2013. Evaluating source code summarization techniques: Replication and expansion. In 2013 21st International Conference on Program Comprehension (ICPC'13). IEEE, 13--22.Google Scholar
- J. Li, H. Xu, X. He, J. Deng, and X. Sun. 2016. Tweet modeling with LSTM recurrent neural networks for hashtag recommendation. IEEE.Google Scholar
- M. Huang, Y. Cao, and C. Dong. 2016. Modeling rich contexts for sentiment classification with LSTM. Arxiv Preprint Arxiv:1605.01478 (2016).Google Scholar
- F. Agostinelli, M. Hoffman, P. Sadowski, and P. Baldi. 2014. Learning activation functions to improve deep neural networks. Arxiv Preprint Arxiv:1412.6830 (2014).Google Scholar
- Z. Ullah, F. Al-Turjman, L. Mostarda, and R. Gagliardi. 2020. Applications of artificial intelligence and machine learning in smart cities. Comput. Commun. (2020).Google Scholar
- A. Severyn and A. Moschitti. 2015. UNITN: Training deep convolutional neural network for Twitter sentiment classification. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval'15). 464--469.Google Scholar
- Z. Zhang. 2018. Improved Adam optimizer for deep neural networks. In 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS'18). IEEE, 1--2.Google Scholar
Cross Ref
Index Terms
A Deep Learning–based Approach for Emotions Classification in Big Corpus of Imbalanced Tweets
Recommendations
Users opinion and emotion understanding in social media regarding COVID-19 vaccine
AbstractOnline social platforms or social platforms such as Twitter, Facebook and Instagram have become popular platforms for a public discussion about social topics. Recent studies show that there is a growing tendency for people to talk about COVID-19 ...
A deep learning-based sentiment analysis approach (MF-CNN-BILSTM) and topic modeling of tweets related to the Ukraine–Russia conflict
AbstractTwitter, one of the most significant social media platforms, can be used as data sources to research public opinion on various topics, including political conflicts. People worldwide have expressed their opinions about the war between ...
Highlights- Sentiment analysis is an effective way to get people's thoughts on the Ukraine–Russia conflict.
Part-of-Speech (POS) Tagging Using Deep Learning-Based Approaches on the Designed Khasi POS Corpus
Part-of-speech (POS) tagging is one of the research challenging fields in natural language processing (NLP). It requires good knowledge of a particular language with large amounts of data or corpora for feature engineering, which can lead to achieving a ...






Comments