REALIZATION OF THE DECISION-MAKING SUPPORT SYSTEM FOR TWITTER USERS’ PUBLICATIONS ANALYSIS
DOI:
https://doi.org/10.15588/1607-3274-2024-1-16Keywords:
natural language processing, convolutional neural network, recurrent neural network, LSTM, k-means clusteringAbstract
Context. The paper emphasizes the need for a decision-making system that can analyze users’ messages and determine the sentiment to understand how news and events impact people’s emotions. Such a system would employ advanced techniques to analyze users’ messages, delving into the sentiment expressed within the text. The primary goal is to gain insights into how news and various events reverberate through people’s emotions.
Objective. The objective is to create a decision-making system that can analyze and determine the sentiment of user messages, understand the emotional response to news and events, and distribute the data into clusters to gain a broader understanding of users’ opinions. This multifaceted objective involves the integration of advanced techniques in natural language processing and machine learning to build a robust decision-making system. The primary goals are sentiment analysis, comprehension of emotional responses to news and events, and data clustering for a holistic view of user opinions.
Method. The use of long-short-term memory neural networks for sentiment analysis and the k-means algorithm for data clustering is proposed for processing large volumes of user data. This strategic combination aims to tackle the challenges posed by processing large volumes of user-generated data in a more nuanced and insightful manner.
Results. The study and conceptual design of the decision-making system have been completed and the decision-making system was created. The system incorporates sentiment analysis and data clustering to understand users’ opinions and the sentiment value of such opinions dividing them into clusters and visualizing the findings.
Conclusions. The conclusion is that the development of a decision-making system capable of analyzing user sentiment and clustering data can provide valuable insights into users’ reactions to news and events in social networks. The proposed use of longshort-term memory neural networks and the k-means algorithm is considered suitable for sentiment analysis and data clustering tasks. The importance of studying existing works and systems to understand available algorithms and their applications is emphasized. The article also describes created and implemented a decision-making system and demonstrated the functionality of the system using a sample dataset.
References
Alhakiem H. R. Aspect-Bas1ed Sentiment Analysis on Twitter Using Logistic Regression with FastText Feature Expansion / Hanif Reangga Alhakiem, Erwin Budi Setiawan // Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi). – 2022. – Vol. 6, No. 5. – P. 840–846. DOI: 10.29207/resti.v6i5.4429
Machine Learning Modeling of Vitality Characteristics in Historical Preservation Zones with Multi-Source Data / [Xiaoran Huang et al.] // Buildings. – 2022. – Vol. 12, No. 11. – P. 1978. DOI: 10.3390/buildings12111978
Lampropoulos G. Virtual Reality in Education: A Comparative Social Media Data and Sentiment Analysis Study / Georgios Lampropoulos, Euclid Keramopoulos // International Journal of Recent Contributions from Engineering, Science & IT (iJES). – 2022. – Vol. 10, No. 03. – P. 19–32. DOI: 10.3991/ijes.v10i03.34057
Wang Y. Synergy Masks of Domain Attribute Model DaBERT: Emotional Tracking on Time-Varying Virtual Space Communication / Ye Wang, Zhenghan Chen, Changzeng Fu // Sensors. – 2022. – Vol. 22, No. 21. – P. 8450. DOI: 10.3390/s22218450
On the development of an information system for monitoring user opinion and its role for the public / [Vladislav Karyukin et al.] // Journal of Big Data. – 2022. – Vol. 9, No. 1. DOI: 10.1186/s40537022-00660-w
A Machine Learning Method for Prediction of Stock Market Using Real-Time Twitter Data / [Saleh Albahli et al.] // Electronics. – 2022. – Vol. 11, No. 20. – P. 3414. DOI: 10.3390/electronics11203414
A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis / [Qizhi Li et al.] // Applied Sciences. – 2022. – Vol. 12, No. 20. – P. 10236. DOI: 10.3390/app122010236
Mobile Applications Rating Performance: A Survey / [Sabreen Abulhaija et al.] // International Journal of Interactive Mobile Technologies (iJIM). – 2022. – Vol. 16, No. 19. – P. 133–146. DOI: 10.3991/ijim.v16i19.32051
N Kamath A. An overview of investor sentiment: Identifying themes, trends, and future direction through bibliometric analysis / Aditi N Kamath, Sandeep S. Shenoy, Subrahmanya Kumar N. // Investment Management and Financial Innovations. – 2022. – Vol. 19, No. 3. – P. 229–242. DOI: 10.21511/imfi.19(3).2022.19
Intelligent Vehicle Sales Prediction Based on Online Public Opinion and Online Search / [Mingyang Zhang et al.] // Sustainability. – 2022. – Vol. 14, No. 16. – P. 10344. DOI: 10.3390/su141610344
Pawełoszek I. Towards a Smart City–The Study of Car-Sharing Services in Poland / Ilona Pawełoszek // Energies. – 2022. – Vol. 15, No. 22. – P. 8459. DOI: 10.3390/en15228459
Public Health Policy Monitoring through Public Perceptions: A Case of COVID-19 Tweet Analysis / [Chih-yuan Li et al.] // Information. – 2022. – Vol. 13, No. 11. – P. 543. DOI: 10.3390/info13110543
Social media analysis of Twitter tweets related to ASD in 2019– 2020, with particular attention to COVID-19: topic modelling and sentiment analysis / [Luca Corti et al.] // Journal of Big Data. – 2022. – Vol. 9, No. 1. DOI: 10.1186/s40537-022-00666-4
Liu H. Online review analysis on various networks’ consumer feedback using deep learning / Huajin Liu // IET Networks. – 2022. – Vol. 11. – P. 234–244. DOI: 10.1049/ntw2.12045
Sentiment Analysis of Twitter Data / [Yili Wang et al.] // Applied Sciences. – 2022. – Vol. 12, No. 22. – P. 11775. DOI: 10.3390/app122211775
Kim D. Graph Convolutional Networks with POS Gate for AspectBased Sentiment Analysis / Dahye Kim, YoungJin Kim, YoungSeob Jeong // Applied Sciences. – 2022. – Vol. 12, No. 19. – P. 10134. DOI: 10.3390/app121910134
Opiła J. On Employing of Extended Characteristic Surface Model for Forecasting of Demand in Tourism / Janusz Opiła // Interdisciplinary Description of Complex Systems. – 2022. – Vol. 20, No. 5. – P. 621–639. DOI: 10.7906/indecs.20.5.8
Bagate R. A. Sarcasm detection of tweets without #sarcasm: data science approach / Rupali Amit Bagate, R. Suguna // Indonesian Journal of Electrical Engineering and Computer Science. – 2021. – Vol. 23, No. 2. – P. 993. DOI: 10.11591/ijeecs.v23.i2.pp993-1001
Identifying key success factors for startups With sentiment analysis using text data mining / [Tina Asgari et al.] // International Journal of Engineering Business Management. – 2022. – Vol. 14. – P. 435– 453. DOI: 10.1177/18479790221131612
Game problem of assigning staff to project implementation / [Agnieszka Kowalska-Styczeń et al.] // Decision Making: Applications in Management and Engineering. – 2023. – Vol. 6, No. 2. – P. 691–721. DOI: 10.31181/dmame622023713
Identification and Correction of Grammatical Errors in Ukrainian Texts Based on Machine Learning Technology / Vasyl Lytvyn [et al.] // Mathematics. – 2023. – Vol. 11, No. 4. – P. 904. DOI: 10.3390/math11040904
Intelligent Analysis of Ukrainian-language Tweets for Public Opinion Research based on NLP Methods and Machine Learning Technology / [Oleh Prokipchuk et al.] // International Journal of Modern Education and Computer Science. – 2023. – Vol. 15, No. 3. – P. 70–93. DOI: 10.5815/ijmecs.2023.03.06
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