Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Nowadays, Twitter is one of the most popular microblogging sites that is generating a massive amount of textual data. Such textual data is intended to incorporate human feelings and opinions with related events like tweets, posts, and status updates. It then becomes difficult to identify and classify the emotions from the tweets due to their restricted word length and data diversity. In contrast, emotion analysis identifies and classifies different emotions based on the text data generated from social media platforms. The underlying work anticipates an efficient category and prediction technique for analyzing different emotions from textual data collected from Twitter. The proposed research work deliberates an enhanced deep neural network (EDNN) based hierarchical Bi-LSTM model for emotion analysis from textual data; that classifies the six emotions mainly sadness, love, joy, surprise, fear, and anger. Furthermore, the emotion analysis result obtained by the proposed hierarchical Bi-LSTM model is being compared and validated with the traditional hybrid CNN-LSTM approach regarding the accuracy, recall, precision, and F1-Score. It can be observed from the results that the proposed hierarchical Bi-LSTM achieves an average accuracy of 89% for emotion analysis, whereas the existing CNN-LSTM model achieved an overall accuracy of 75%. This result shows that the proposed hierarchical Bi-LSTM approach achieves desired performance compared to the CNN-LSTM model.
Rocznik
Tom
Strony
art. no. e141001
Opis fizyczny
Bibliogr. 33 poz., rys., tab.
Twórcy
autor
- Department of Computer Science and Technology, Central University of Jharkhand, Ranchi, India
autor
- Department of Computer Science and Technology, Central University of Jharkhand, Ranchi, India
Bibliografia
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- [3] X. Ye, H. Dai, L.-A. Dong, and X. Wang, “Multi-view ensemble learning method for microblog sentiment classification,” Expert Syst. Appl., vol. 166, p. 113987, 2021, doi: 10.1016/j.eswa.2020.113987.
- [4] S.M. Basha and D.S. Rajput, “An innovative topic-based customer complaints sentiment classification system,” Int. J. Bus. Innov. Res., vol. 20, no. 3, pp. 375–391, 2019, doi: 10.1504/IJBIR.2019.10013706.
- [5] S.M. Basha and D.S. Rajput, “A supervised aspect level sentiment model to predict overall sentiment on tweeter documents,” Int. J. Metadata Semant. Ontol., vol. 13, no. 1, pp. 33–41, 2018, doi: 10.1504/IJMSO.2018.096451.
- [6] C. Diamantini, A. Mircoli, D. Potena, and E. Storti, “Social information discovery enhanced by sentiment analysis techniques,” Future Gener. Comput. Syst., vol. 95, pp. 816–828, 2019, doi: 10.1016/j.future.2018.01.051.
- [7] S.M. Basha and D.S. Rajput, “A roadmap towards implementing parallel aspect level sentiment analysis,” Multimed. Tools Appl., vol. 78, no. 20, pp. 29 463–29 492, 2019, doi: 10.1007/s11042-018-7093-z.
- [8] L.F.S. Coletta, N.F.F. da Silva, E.R. Hruschka, and E.R. Hruschka, “Combining classification and clustering for tweet sentiment analysis,” in 2014 Brazilian Conference on Intelligent Systems, 2014, pp. 210–215, doi: 10.1109/BRACIS.2014.46.
- [9] E.S. Tellez, S. Miranda-Jiménez, M. Graff, D. Moctezuma, O.S. Siordia, and E.A. Villaseñor, “A case study of Spanish text transformations for twitter sentiment analysis,” Expert Syst. Appl., vol. 81, pp. 457–471, 2017, doi: 10.1016/j.eswa.2017.03.071.
- [10] S.M. Basha and D.S. Rajput, “Parsing based sarcasm detection from literal language in tweets,” Recent Pat. Comput. Sci., vol. 11, no. 1, pp. 62–69, 2018, doi: 10.2174/2213275911666180531112306.
- [11] P. Kumar and R.S. Thakur, “Liver disorder detection using variable-neighbor weighted fuzzy k nearest neighbor approach,” Multimed. Tools Appl., vol. 80, no. 11, pp. 16 515–16 535, 2021, doi: 10.1007/s11042-019-07978-3.
- [12] P. Kumar and R.S. Thakur, “An approach using fuzzy sets and boosting techniques to predict liver disease,” CMC-Comput. Mater. Continua, vol. 68, no. 3, pp. 3513–3529, 2021, doi: 10.32604/cmc.2021.016957.
- [13] D. Mahto and S. Nair, “A review on smart bulb & proposed a real time vision based smart bulb using image processing,” Int. J. Eng. Trends Technol (IJETT), vol. 44, no. 4, pp. 195–201, 2017, doi: 10.14445/22315381/IJETT-V44P239.
- [14] P. Kumar and R.S. Thakur, “Diagnosis of liver disorder using fuzzy adaptive and neighbor weighted k-nn method for lft imbalanced data,” in 2019 International Conference on Smart Structures and Systems (ICSSS), 2019, pp. 1–5, doi: 10.1109/ICSSS.2019.8882861.
- [15] D. Mahto and S. Nair, “A real time vision based smart bulb using image processing,” Int. J. Comput. Appl., vol. 164, no. 11, pp. 46–54, 2017, doi: 10.5120/ijca2017913412.
- [16] W.Y. Chong, B. Selvaretnam, and L.-K. Soon, “Natural language processing for sentiment analysis: an exploratory analysis on tweets,” in 2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology, 2014, pp. 212–217, doi: 10.1109/ICAIET.2014.43.
- [17] N. Balaganesh and K. Muneeswaran, “Aspect-based sentiment classification model employing whale-optimized adaptive neural network,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 3, p. e137271, 2021, doi: 10.24425/bpasts.2021.13727.
- [18] V.V. Kolisetty and D.S. Rajput, “A review on the significance of machine learning for data analysis in big data,” Jordanian J. Comput. Inf. Technol. (JJCIT), vol. 6, no. 01, pp. 41–57, 2020, doi: 10.5455/jjcit.71-1564729835.
- [19] Ž. Nedeljković, M. Milošević, and Ž. Ðurović, “Analysis of features and classifiers in emotion recognition systems: Case study of slavic languages,” Arch. Acoust., vol. 45, no. 1, pp. 129–140, 2020, doi: 10.24425/aoa.2020.132489.
- [20] A. Tripathy, A. Agrawal, and S.K. Rath, “Classification of sentimental reviews using machine learning techniques,” Procedia Comput. Sci., vol. 57, pp. 821–829, 2015, doi: 10.1016/j.procs.2015.07.523.
- [21] T.M. Untawale and G. Choudhari, “Implementation of sentiment classification of movie reviews by supervised machine learning approaches,” in 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), 2019, pp. 1197–1200, doi: 10.1109/ICCMC.2019.8819800.
- [22] G.T. Reddy, M.P.K. Reddy, K. Lakshmanna, D.S. Rajput, R. Kaluri, and G. Srivastava, “Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis,” Evol. Intell., vol. 13, no. 2, pp. 185–196, 2020, doi: 10.1007/s12065-019-00327-1.
- [23] D. Xu, Z. Tian, R. Lai, X. Kong, Z. Tan, andW. Shi, “Deep learning based emotion analysis of microblog texts,” Information Fusion, vol. 64, pp. 1–11, 2020, doi: 10.1016/j.inffus.2020.06.002.
- [24] X. Wang, L. Kou, V. Sugumaran, X. Luo, and H. Zhang, “Emotion correlation mining through deep learning models on natural language text,” IEEE Trans. Cybern., vol. 51, no. 9, pp. 4400–4413, 2021, doi: 10.1109/TCYB.2020.2987064.
- [25] G. Xu, W. Li, and J. Liu, “A social emotion classification approach using multi-model fusion,” Future Gener. Comput. Syst., vol. 102, pp. 347–356, 2020, doi: 10.1016/j.future.2019.07.007.
- [26] A. Chatterjee, U. Gupta, M.K. Chinnakotla, R. Srikanth, M. Galley, and P. Agrawal, “Understanding emotions in text using deep learning and big data,” Comput. Hum. Behav., vol. 93, pp. 309–317, 2019, doi: 10.1016/j.chb.2018.12.029.
- [27] M.S. Akhtar, D.S. Chauhan, D. Ghosal, S. Poria, A. Ekbal, and P. Bhattacharyya, “Multi-task learning for multi-modal emotion recognition and sentiment analysis,” arXiv preprint arXiv:1905.05812, 2019, doi: 10.48550/arXiv.1905.05812.
- [28] S. Poria, N. Majumder, R. Mihalcea, and E. Hovy, “Emotion recognition in conversation: Research challenges, datasets, and recent advances,” IEEE Access, vol. 7, pp. 100 943–100 953, 2019, doi: 10.1109/ACCESS.2019.2929050.
- [29] M.S.K. Reddy and D.S. Rajput, “Ternary-based feature level extraction for anomaly detection in semantic graphs: an optimal feature selection basis,” Sādhanā, vol. 46, no. 1, pp. 1–16, 2021, doi: 10.1007/s12046-021-01570-y.
- [30] J. Zhou, Y. Lu, H.-N. Dai, H. Wang, and H. Xiao, “Sentiment analysis of chinese microblog based on stacked bidirectional lstm,” IEEE Access, vol. 7, pp. 38 856–38 866, 2019, doi: 10.1109/ACCESS.2019.2905048.
- [31] G. Mourgias-Alexandris, A. Tsakyridis, N. Passalis, A. Tefas, K. Vyrsokinos, and N. Pleros, “An all-optical neuron with sigmoid activation function,” Opt. Express, vol. 27, no. 7, pp. 9620–9630, 2019, doi: 10.1364/OE.27.009620.
- [32] T.R. Gadekallu, et al. “A novel pca–whale optimization-based deep neural network model for classification of tomato plant diseases using gpu,” J. Real-Time Image Process., vol. 18, no. 4, pp. 1383–1396, 2021, doi: 10.1007/s11554-020-00987-8.
- [33] D.M. Powers, “Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation,” arXiv preprint arXiv:2010.16061, 2020, doi: 10.48550/arXiv.2010.16061.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e2a274ca-b920-4380-b3e7-5f3afffe2a39