Methodology for Deducing Political Attitudes from Tweets for the Purpose of Predicting Their Popularity
Keywords:
Political Sentiment Analysis, Deep Learning, Machine Learning, Natural Language Processing, RNN, LSTMAbstract
In today's digital age, people can express themselves virtually through social media. In reality, businesses can't afford to ignore social feedback if they want to expand organically. Sharing one's feelings on social media also has the power to sway the opinions of others. Businesses from many walks of life have taken notice of social media because of this feature. One arena where public opinion has the potential to alter the course of political parties is politics. With over 500 million tweets sent daily and 330 million users, Twitter is a prominent microblogging platform that reflects people's political beliefs. Many different feelings are expressed in Twitter's User Generated Content (UGC). To find political attitudes and forecast party popularity, we presented a deep learning framework called the Political Opinion Mining Framework (POMF) in this article. Not only does the framework optimise training, but it also incorporates an innovative and thorough feature selection approach. To improve training quality, it uses Natural Language Processing (NLP) to gather non-linguistic contextual information. The main technique, called Deep Political Sentiment Discovery (DPSD), uses a refined version of a Recurrent Neural Network (RNN) that relies on Long Short-Term Memory (LSTM). Several cutting-edge machine learning algorithms are tested to see how the suggested framework performs. The DPSD proves to be more effective than conventional machine learning methods, according to the empirical investigation.
Downloads
References
Poria, S., Cambria, E., Gelbukh, A.: Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Syst. 108, 42–49 (2016). https://doi.org/10.1016/j.knosys.2016.06.009.
Balazs, J.A., Velásquez, J.D.: Opinion Mining and Information Fusion: A survey. Inf. Fusion. 27, 95–110 (2016). https://doi.org/10.1016/j.inffus.2015.06.002.
Zhu, C., Cheng, G., Wang, K.: Big Data Analytics for Program Popularity Prediction in Broadcast TV Industries. IEEE Access. 5, 24593–24601 (2017). https://doi.org/10.1109/ACCESS.2017.2767104.
Manek, A.S., Shenoy, P.D., Mohan, M.C., Venugopal, K.R.: Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web. 20, 135–154 (2017). https://doi.org/10.1007/s11280-015-0381-x.
Sun, S., Luo, C., Chen, J.: A review of natural language processing techniques for opinion mining systems. Inf. Fusion. 36, 10–25 (2017). https://doi.org/10.1016/j.inffus.2016.10.004.
Beigi G., Hu X., Maciejewski R., Liu H. (2016) An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief. In: Pedrycz W., Chen SM. (eds) Sentiment Analysis and Ontology Engineering. Studies in Computational Intelligence, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-30319-2_13.
Soleymani, M., Garcia, D., Jou, B., Schuller, B., Chang, S.F., Pantic, M.: A survey of multimodal sentiment analysis. Image Vis. Comput. 65, 3–14 (2017). https://doi.org/10.1016/j.imavis.2017.08.003.
Giatsoglou, M., Vozalis, M.G., Diamantaras, K., Vakali, A., Sarigiannidis, G., Chatzisavvas, K.C.: Sentiment analysis leveraging emotions and word embeddings. Expert Syst. Appl. 69, 214–224 (2017). https://doi.org/10.1016/j.eswa.2016.10.043.
Chaturvedi, I., Cambria, E., Welsch, R.E., Herrera, F.: Distinguishing between facts and opinions for sentiment analysis: Survey and challenges. Inf. Fusion. 44, 65–77 (2018). https://doi.org/10.1016/j.inffus.2017.12.006.
Dragoni, M., Poria, S., Cambria, E.: OntoSenticNet: A Commonsense Ontology for Sentiment Analysis. IEEE Intell. Syst. 33, 77–85 (2018). https://doi.org/10.1109/MIS.2018.033001419.
Cambria, E., Gastaldo, P., Bisio, F., Zunino, R.: An ELM-based model for affective analogical reasoning. Neurocomputing. 149, 443–455 (2015). https://doi.org/10.1016/j.neucom.2014.01.064.
Dehkharghani, R., Saygin, Y., Yanikoglu, B., Oflazer, K.: SentiTurkNet: a Turkish polarity lexicon for sentiment analysis. Lang. Resour. Eval. 50, 667–685 (2016). https://doi.org/10.1007/s10579-015-9307-6.
Yaqub, U., Chun, S.A., Atluri, V., Vaidya, J.: Analysis of political discourse on twitter in the context of the 2016 US presidential elections. Gov. Inf. Q. 34, 613–626 (2017). https://doi.org/10.1016/j.giq.2017.11.001.
Petz, G., Karpowicz, M., Fürschuß, H., Auinger, A., Stříteský, V., Holzinger, A.: Computational approaches for mining user’s opinions on the Web 2.0. Inf. Process. Manag. 50, 899–908 (2014). https://doi.org/10.1016/j.ipm.2014.07.005.
Chen, T., Xu, R., He, Y., Xia, Y., & Wang, X. (2016). Learning User and Product Distributed Representations Using a Sequence Model for Sentiment Analysis. IEEE Computational Intelligence Magazine, 11(3), p34–44. doi: 10.1109/MCI.2016.2572539.
Vilares, D., Alonso, M.A., Gómez-Rodríguez, C.: Supervised sentiment analysis in multilingual environments. Inf. Process. Manag. 53, 595–607 (2017). https://doi.org/10.1016/j.ipm.2017.01.004.
Stieglitz, S., Meske, C., Ross, B., Mirbabaie, M.: Going Back in Time to Predict the Future - The Complex Role of the Data Collection Period in Social Media Analytics. Inf. Syst. Front. 22, 395–409 (2020). https://doi.org/10.1007/s10796-018-9867-2.
Nakov, P., Rosenthal, S., Kiritchenko, S., Mohammad, S.M., Kozareva, Z., Ritter, A., Stoyanov, V., Zhu, X.: Developing a successful SemEval task in sentiment analysis of Twitter and other social media texts. Lang. Resour. Eval. 50, 35–65 (2016). https://doi.org/10.1007/s10579-015-9328-1.
Fan, Z.P., Che, Y.J., Chen, Z.Y.: Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis. J. Bus. Res. 74, 90–100 (2017). https://doi.org/10.1016/j.jbusres.2017.01.010.
Bouazizi, M., Otsuki, T.: A Pattern-Based Approach for Sarcasm Detection on Twitter. IEEE Access. 4, 5477–5488 (2016). https://doi.org/10.1109/ACCESS.2016.2594194.
Hai, Z., Cong, G., Chang, K., Cheng, P., Miao, C.: Analyzing sentiments in one go: A supervised joint topic modeling approach. IEEE Trans. Knowl. Data Eng. 29, 1172–1185 (2017). https://doi.org/10.1109/TKDE.2017.2669027.
Lin, C., He, Y., Everson, R., Rüger, S.: Weakly supervised joint sentiment-topic detection from text. IEEE Trans. Knowl. Data Eng. 24, 1134–1145 (2012). https://doi.org/10.1109/TKDE.2011.48.
Dermouche, M., Velcin, J., Khouas, L., Loudcher, S., Dermouche, M., Velcin, J., Khouas, L., Loudcher, S., Model, A.J., Dermouche, M., Velcin, J., Khouas, L., Loudcher, S.: A Joint Model for Topic-Sentiment Evolution over Time To cite this version : HAL Id : hal-01762995 A Joint Model for Topic-Sentiment Evolution over Time. (2018).
Hai, Z., Chang, K., Kim, J.J., Yang, C.C.: Identifying Features in Opinion Mining via Intrinsic and Extrinsic Domain Relevance. IEEE Trans. Knowl. Data Eng. 26, 623–634 (2014). https://doi.org/10.1109/TKDE.2013.26.
Tang, D., Wei, F., Qin, B., Yang, N., Liu, T., Zhou, M.: Sentiment Embeddings with Applications to Sentiment Analysis. IEEE Trans. Knowl. Data Eng. 28, 496–509 (2016). https://doi.org/10.1109/TKDE.2015.2489653.
Lim, K.W., Chen, C., Buntine, W.: Twitter-Network Topic Model: A Full Bayesian Treatment for Social Network and Text Modeling. 1–6 (2016).
Sultanova, N., Kozhakhmet, K., Jantayev, R., Botbayeva, A.: Stemming algorithm for kazakh language using rule-based approach. 2019 15th Int. Conf. Electron. Comput. Comput. ICECCO 2019. 3–6 (2019). https://doi.org/10.1109/ICECCO48375.2019.9043253.
M. Nandathilaka, S. Ahangama and G. T. Weerasuriya, "A Rule-based Lemmatizing Approach for Sinhala Language," 2018 3rd International Conference on Information Technology Research (ICITR), Moratuwa, Sri Lanka, 2018, pp. 1-5, doi: 10.1109/ICITR.2018.8736134.
Manek, A. S., Shenoy, P. D., Mohan, M. C., & R, V. K. (2016). Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web, 20(2), 135–154. https://doi.org/10.1007/s11280-015-0381-x.
Luo, F., Li, C., Cao, Z.: Affective-feature-based sentiment analysis using SVM classifier. Proc. 2016 IEEE 20th Int. Conf. Comput. Support. Coop. Work Des. CSCWD 2016. 276–281 (2016). https://doi.org/10.1109/CSCWD.2016.7566001.
Rao, P.S., Srinivas, K., Mohan, A.K.: Stock market prices prediction using random forest and extra tree regression. Int. J. Recent Technol. Eng. (IJRTE) 8(3), 1224–1228 (2019).
Bibi, R.: Sentiment A nalysis for Urdu N ews Tweets U sing Decision T ree. 2019 IEEE 17th Int. Conf. Softw. Eng. Res. Manag. Appl. 66–70 (2019).
Rao P.S., Srinivas K., Mohan A.K. (2020) A Survey on Stock Market Prediction Using Machine Learning Techniques. In: Kumar A., Paprzycki M., Gunjan V. (eds) ICDSMLA 2019. Lecture Notes in Electrical Engineering, vol 601. Springer, Singapore. https://doi.org/10.1007/978-981-15-1420-3_101.
Bayhaqy, A., Sfenrianto, S., Nainggolan, K., Kaburuan, E.R.: Sentiment Analysis about E-Commerce from Tweets Using Decision Tree, K-Nearest Neighbor, and Naïve Bayes. 2018 Int. Conf. Orange Technol. ICOT 2018. 1–6 (2018). https://doi.org/10.1109/ICOT.2018.8705796.
Bhargava, K., Katarya, R.: An improved lexicon using logistic regression for sentiment analysis. 2017 Int. Conf. Comput. Commun. Technol. Smart Nation, IC3TSN 2017. 2017-Octob, 332–337 (2018). https://doi.org/10.1109/IC3TSN.2017.8284501.
Ramadhan, W.P., Novianty, A., Setianingsih, C.: Sentiment analysis using multinomial logistic regression. ICCREC 2017 - 2017 Int. Conf. Control. Electron. Renew. Energy, Commun. Proc. 2017-Janua, 46–49 (2017). https://doi.org/10.1109/ICCEREC.2017.8226700.
Al Amrani, Y., Lazaar, M., & El Kadiri, K. E. (2018). Random Forest and Support Vector Machine based Hybrid Approach to Sentiment Analysis. Procedia Computer Science, 127, 511–520.
I. Lakhsmi Manikyamba, Dr. A. Krishna Mohan. Novel Adaptive Multi Label Learning Classification to predict political lenience over online social networks, Adv. Res. Dyn. Control Syst. 12, 1467–1476 (2020). DOI: 10.5373/JARDCS/V12I2/S20201344.
Singh, S.N., Sarraf, T.: Sentiment analysis of a product based on user reviews using random forests algorithm. Proc. Conflu. 2020 - 10th Int. Conf. Cloud Comput. Data Sci. Eng. 112–116 (2020). https://doi.org/10.1109/Confluence47617.2020.9058128.
Ranjit, S., Shrestha, S., Subedi, S., Shakya, S.: Foreign Rate Exchange Prediction Using Neural Network and Sentiment Analysis. Proc. - IEEE 2018 Int. Conf. Adv. Comput. Commun. Control Networking, ICACCCN 2018. 1173–1177 (2018). https://doi.org/10.1109/ICACCCN.2018.8748819.
De S Santos, R.L., De Sousa, R.F., Rabelo, R.A.L., Moura, R.S.: An experimental study based on Fuzzy Systems and Artificial Neural Netwo rks to estimate the importance of reviews about product and services. Proc. Int. Jt. Conf. Neural Networks. 2016-Octob, 647–653 (2016).
Polamuri, S.R., Srinivas, K. & Mohan, A.K. Multi model-Based Hybrid Prediction Algorithm (MM-HPA) for Stock Market Prices Prediction Framework (SMPPF). Arab J Sci Eng (2020). https://doi.org/10.1007/s13369-020-04782-2.
Manikyamba, I.L., Mohan, A.K.: Analyzing Political Trending Tweets for Opinion Extraction. Int. J. Recent Technol. Eng. 8, 2746–2752 (2020). https://doi.org/10.35940/ijrte.f8197.038620.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
