Classification Users' Sentiments About The Threads Application Using Machine And Deep Learning
DOI:
https://doi.org/10.61856/c1232843Keywords:
Natural Language Processing, Sentiment Analysis, Threads Apps, Deep LearningAbstract
Sentiment analysis is a crucial tool in our data-driven world. It is considered Sentiment analysis is an important topic in the field of natural language processing(NLP). With the increasing volume of text data flowing through social media platforms and review sites. Through it, users' opinions and feelings about a specific product or topic can be understood and these comments can be analyzed into negative, good, and neutral. Our study aims to classify the opinions and feelings of twitter users about one of Meta's applications, which is the (Thread) application, using machine learning algorithms and deep learning networks. The dataset was obtained from the largest data collection is from the (Kaggle) website, as this data set contains reviews of application users. Using recurrent neural networks that have shown good results compared to machine learning algorithms. Satisfactory results were obtained according to the evaluation metrics used is (Confusion matrix,F1-score ,Accuracy, Precision and Recall). This study contributes to the field of natural language processing by providing insights into users' experiences with the Thread app. These findings enable app developers to understand user perceptions and improve their experience. Using sentiment analysis enables companies to understand what customers are saying about their products and services, helping them accurately identify strengths and weaknesses.
References
Alam, N., & Abdi, M. (n.d.). Sentiment Analysis of Threads Application Users Using Support Vector Machine Method.
Dang, C. N., Moreno-García, M. N., & De La Prieta, F. (2021). Hybrid Deep Learning Models for Sentiment Analysis. Complexity, 2021(1), 9986920. https://doi.org/10.1155/2021/9986920
Dong, Y., Fu, Y., Wang, L., Chen, Y., Dong, Y., & Li, J. (2020). A Sentiment Analysis Method of Capsule Network Based on BiLSTM. IEEE Access, 8, 37014–37020. https://doi.org/10.1109/ACCESS.2020.2973711
Guia, M., Silva, R., & Bernardino, J. (2019). Comparison of Naïve Bayes, Support Vector Machine, Decision Trees and Random Forest on Sentiment Analysis: Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 525–531. https://doi.org/10.5220/0008364105250531
Kambayo, A. N., Berutu, S. S., & Nshimiyimana, A. (n.d.). Aspect-Based Sentiment Analysis Using Latent Dirichlet Allocation (LDA) and DistilBERT on Threads App Reviews.
Khairunnas, R., Pagua, J. A., Fitriya, G., & Ruldeviyani, Y. (2025). User sentiment dynamics in social media: A comparative analysis of X and Threads. IAES International Journal of Artificial Intelligence (IJ-AI), 14(1), 447. https://doi.org/10.11591/ijai.v14.i1.pp447-456
Lestari, D. W., & Ambarwati, R. (2024). Social Network Analysis: Understanding User Behavior in Threads. Kontigensi : Jurnal Ilmiah Manajemen, 12(1), 115–124. https://doi.org/10.56457/jimk.v12i1.505
Madyatmadja, E. D., Candra, H., Nathaniel, J., Jonathan, M. R., & Rudy. (2024). Sentiment Analysis on User Reviews of Threads Applications in Indonesia. Journal Européen Des Systèmes Automatisés, 57(4), 1165–1171. https://doi.org/10.18280/jesa.570423
Sachin, S., Tripathi, A., Mahajan, N., Aggarwal, S., & Nagrath, P. (2020). Sentiment Analysis Using Gated Recurrent Neural Networks. SN Computer Science, 1(2), 74. https://doi.org/10.1007/s42979-020-0076-y
Srinivas, A. C. M. V., Satyanarayana, Ch., Divakar, Ch., & Sirisha, K. P. (2021). Sentiment Analysis using Neural Network and LSTM. IOP Conference Series: Materials Science and Engineering, 1074(1), 012007. https://doi.org/10.1088/1757-899X/1074/1/012007
Student, Department of Computer Engineering, JSPM’s Rajarshi Shahu College of Engineering. Tathawade (Maharashtra), India., Ghumade*, Ms. T. G., Deshmukh, Mrs. R., & Department of Computer Engineering, JSPM’s Rajarshi Shahu College of Engineering. Tathawade (Maharashtra), India. (2019). A Document Classification using NLP and Recurrent Neural Network. International Journal of Engineering and Advanced Technology, 8(6), 633–636. https://doi.org/10.35940/ijeat.F8087.088619
Suhaimi, N. B. A., & Lestari, M. (2024). Sentiment Analysis of Tiktok App Reviews on Google Play using Several Machine Learning Methods. International Journal of Global Operations Research, 5(4), 275–287. https://doi.org/10.47194/ijgor.v5i4.343
Sunagar, P., Sowmya, B. J., Pruthviraja, D., Supreeth, S., Mathew, J., Rohith, S., & Shruthi, G. (2024). Hybrid RNN Based Text Classification Model for Unstructured Data. SN Computer Science, 5(6), 726. https://doi.org/10.1007/s42979-024-03091-x
Varadharajan, V., Smith, N., Kalla, D., Samaah, F., & Mandala, V. (2025). Deep Learning-Based Sentiment Analysis: Enhancing IMDb Review Classification with LSTM Models. Universal Journal of Computer Sciences and Communications, 4(1), 1–14. https://doi.org/10.31586/ujcsc.2025.1249
Zhang, P., He, Y., Haq, E.-U., He, J., & Tyson, G. (2024). The Emergence of Threads: The Birth of a New Social Network (No. arXiv:2406.19277). arXiv. https://doi.org/10.48550/arXiv.2406.19277
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