Analyzing Social Media Sentiment: Twitter as a Case Study
Abstract This study examines the problem of Twitter sentimental analysis, which categorizes Tweets as positive or negative. Many applications require analyzing public mood, including organizations attempting to determine the market response to their products, political election forecasting, and macroeconomic phenomena such as stock exchange forecasting. Twitter is a social networking microblogging and digital platform that allows users to update their status in a maximum of 140 characters. It is a rapidly expanding platform with over 200 million registered users, 100 million active users, and half of the people log on every day, tweeting out over 250 million tweets. Public opinion analysis is critical for applications, including firms looking to understand market responses to their products, predict political choices, and forecast socio-economic phenomena like bonds. Through the deep learning methodologies, a recurrent neural network with convolutional neural network models was constructed to do Twitter sentiment analysis to predict if a tweet is positive or negative using a dataset of tweets. The applied methods were trained using a publicly available dataset of 1,600,000 tweets. Several model architectures were trained, with the best one achieving a (93.91%) success rate in recognizing the tweets' matching sentiment. The model's high success rate makes it a valuable advisor and a technique that might be improved to enable an integrated sentiment analyzer system that can work in real-world situations for political marketing.
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Abdullah, A., and Mohammed, H. 2019. Social Network Privacy Models. Cihan University-Erbil Scientific Journal, 3(2), 92–101.
Ajay, Sh., and Ausif, M. 2019. Review of deep learning algorithms and architectures. IEEE access, 7, 53040–53065.
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Ali, N., Elnagar, A., Shahin, I., and Henno, S. 2021. Deep learning for Arabic subjective sentiment analysis: Challenges and research opportunities. Applied Soft Computing, 98, 106836.
Andreas, S., and Faulkner, Ch. 2017. NLP: the new technology of achievement, NLP Comprehensive (Organization), New York, Morrow.
Arvinder, B., Mexson, F., Choubey, S., and Goel, M. 2019. Comparative performance of machine learning algorithms for fake news detection. In International conference on advances in computing and data sciences (pp. 420–430). Springer, Singapore.
Asil, C., Dilek, H., and Junlan, F. 2010. Probabilistic model-based sentiment analysis of twitter messages. In 2010 IEEE Spoken Language Technology Workshop (pp. 79–84). IEEE.
Balakrishnan, G., Priyanthan, P., Ragavan, T., Prasath, N., and Perera, A. 2012. Opinion mining and sentiment analysis on a twitter data stream. In International conference on advances in ICT for emerging regions (ICTer2012) (pp. 182–188). IEEE.
Chigozie, N., Ijomah, W., Gachagan, A., and Marshall, S. 2018. Activation functions: Comparison of trends in practice and research for deep learning. arXiv preprint arXiv:1811.03378.
Christopher, C. 2020. Improving t-SNE for applications on word embedding data in text mining.
Deborah, W., Ardoin, N., and Gould, R. 2021. Using social network analysis to explore and expand our understanding of a robust environmental learning landscape. Environmental Education Research, 27(9), 1263–1283.
Fahad, M., Ahmed, A., and Abdulbasit, S. 2020. Smiling and non-smiling emotion recognition based on lower-half face using deep-learning as convolutional neural network. In Proceedings of the 1st International Multi-Disciplinary Conference Theme: Sustainable Development and Smart Planning, IMDC-SDSP 2020, Cyperspace, 28–30 June 2020.
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Georgios, G., Vakali, A., Diamantaras, K., and Karadais, P. 2019. Behind the cues: A benchmarking study for fake news detection. Expert Systems with Applications, 128, 201–213.
Govindarajan, M. 2021. Educational Data Mining Techniques and Applications. In Advancing the Power of Learning Analytics and Big Data in Education (pp. 234–251). IGI Global.
Guy, H., Kok, H., Chandra, R., Razavi, A., Huang, S., Brooks, M., and Asadi, H. 2019. Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. American Journal of Roentgenology, 212(1), 38–43.
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Jan, L., Mohammadi, M., Mohammed, A., Karim, S., Rashidi, S., Rahmani, A., and Hosseinzadeh, M. 2021. A survey of deep learning techniques for misuse-based intrusion detection systems.
Jasim, Y, and Mustafa, S. 2018. Developing a software for diagnosing heart disease via data mining techniques.
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Judith, B., Michael, B., Danica, K., and Kjellstrom, H. 2017. Deep representation learning for human motion prediction and classification. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6158–6166).
Manuel, R., Gütl, C., and Pietroszek, K. 2021. Real-time gesture animation generation from speech for virtual human interaction. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1–4).
Marina, P., and Emanuele, F. 2020. Multidisciplinary pattern recognition applications: a review. Computer Science Review, 37, 100276.
Matthew, C., Castelfranco, A., Roncalli, V., Lenz, P, and Hartline, D. 2020. t-Distributed Stochastic Neighbor Embedding (t-SNE): A tool for eco-physiological transcriptomic analysis. Marine genomics, 51, 100723.
Mohammed, Sh. 2019. Design a Mobile Medication Dispenser based on IoT Technology. International Journal of Innovation, Creativity and Change, 6(2), 242–250.
Mohammed, Sh. 2021. Improving Coronavirus Disease Tracking in Malaysian Health System. Cihan University-Erbil Scientific Journal, 5(1), 11–19.
Mohammed, T., and Jose, C. 2020. Embeddings in natural language processing: Theory and advances in vector representations of meaning. Synthesis Lectures on Human Language Technologies, 13(4), 1–175.
Muhamad, A., Yaser, J., Mostafa, W., Mustafa S., and Sadeeer A. 2021. High-Performance Deep learning to Detection and Tracking Tomato Plant Leaf Predict Disease and Expert Systems.
Mustafa, A., Yaser, J., Tawfeeq, F., and Mustafa, S. 2021. On Announcement for University Whiteboard Using Mobile Application. CSRID (Computer Science Research and Its Development Journal), 12(1), 64–79.
Nasim, R., Baratin, A., Arpit, D., Draxler, F., Lin, M., Hamprecht, F., and Courville, A. 2019. On the spectral bias of neural networks. In International Conference on Machine Learning (pp. 5301–5310). PMLR.
Neethu, S., and Rajasree, R. 2013. Sentiment analysis in twitter using machine learning techniques. In 2013 fourth international conference on computing, communications and networking technologies (ICCCNT) (pp. 1–5). IEEE.
Neha, K., and Bhilare, P. 2015. An Approach for Sentiment analysis on social networking sites. In 2015 International Conference on Computing Communication Control and Automation (pp. 390–395). IEEE.
Nik, H., Prester, J., and Wagner, G. 2020. Seeking Out Clear and Unique Information Systems Concepts: A Natural Language Processing Approach. In ECIS.
Nikhil, B. 2017. Fundamentals of Deep Learning: Designing Next-generation Artificial Intelligence Algorithms/c Nikhil Buduma. Beijing, Boston, Farnham, Sebastopol, Tokyo: OReilly.
Niloufar, Sh., Shoeibi, N., Hernández, G., Chamoso, P., and Juan, C. 2021. AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter. Electronics, 10(24), 3081.
Peng, Ch., Sun, Z., Lidong B., and Wei, Y. 2017. Recurrent attention network on memory for aspect sentiment analysis. In Proceedings of the 2017 conference on empirical methods in natural language processing (pp. 452–461).
Priyanka, D., and Silakari, S. 2021. Deep learning algorithms for cybersecurity applications: A technological and status review. Computer Science Review, 39, 100317.
Rohit, K. 2018. Fake news detection using a deep neural network. In 2018 4th International Conference on Computing Communication and Automation (ICCCA) (pp. 1–7). IEEE.
Safwan, H., and Yaser, J. 2013. Diagnosis Windows Problems Based on Hybrid Intelligence Systems. Journal of Engineering Science & Technology (JESTEC), 8(5), 566–578.
Sagar, B., Doshi, A., Doshi, U., and Narvekar, M. 2014. A review of techniques for sentiment analysis of twitter data. In 2014 International conference on issues and challenges in intelligent computing techniques (ICICT) (pp. 583–591). IEEE.
Seyed-Ali, B., and Andreas, D. 2013. Sentiment analysis using sentiment features. In 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) (Vol. 3, pp. 26–29). IEEE.
Sherry, G., Amer, E., and Gadallah, M. 2018. Deep learning algorithms for detecting fake news in online text. In 2018 13th international conference on computer engineering and systems (ICCES) (pp. 93–97). IEEE.
Thabit, Th., and Jasim, Y. 2017. Applying IT in Accounting, Environment and Computer Science Studies. Scholars' Press.
Thabit, Th., and Yaser, J. 2015. A manuscript of knowledge representation. International Journal of Human Resource & Industrial Research, 4(4), 10–21.
Thabit, Th., and Yaser, J. 2017. The role of social networks in increasing the activity of E-learning. In Social Media Shaping e-Publishing and Academia (pp. 35–45). Springer, Cham.
Xipeng, Q., Sun, T., Xu, Y., Shao, Y., Dai, N., and Huang, X. 2020. Pre-trained models for natural language processing: A survey. Science China Technological Sciences, 63(10), 1872–1897.
Yaser, J. 2018. Improving intrusion detection systems using artificial neural networks. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 7(1), 49–65.
Yaser, J., Mustafa, O., and Mustafa, S. 2021. Designing and implementation of a security system via UML: smart doors. CSRID (Computer Science Research and Its Development Journal), 12(1), 01–22.
Jasim, Y. A., Saeed, M. G., & Raewf, M. B. (2023). Analyzing Social Media Sentiment: Twitter as a Case Study. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(4), 427–450. https://doi.org/10.14201/adcaij.28394
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