SENTIMENT LABELING AND TEXT CLASSIFICATION MACHINE LEARNING FOR WHATSAPP GROUP
Abstract
The use of WhatsApp Group (WAG) for communication is increasing nowadays. WAG communication data can be analyzed from various perspectives. However, this data is imported in the form of unstructured text files. The aim of this research is to explore the potential use of the SentiwordNet lexicon for labeling the positive, negative, or neutral sentiment of WAG data from "Alumni94" and training and testing it with machine learning text classification models. The training and testing were conducted on six models, namely Random Forest, Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), Linear Support Vector Machine (SVM), and Artificial Neural Network. The labeling results indicate that neutral sentiment is the majority with 7588 samples, followed by 324 negative and 1617 positive samples. Among all the models, Random Forest showed better precision and recall, i.e., 83% and 64%. On the other hand, Decision Tree had slightly lower precision and recall, i.e., 80% and 66%, but exhibited a better f-measure of 71%. The accuracy evaluation results of the Random Forest and Decision Tree models showed significant performance compared to others, achieving an accuracy of 89% in classifying new messages. This research demonstrates the potential use of the SentiwordNet lexicon and machine learning in sentiment analysis of WAG data using the Random Forest and Decision Tree models
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References
M. P. Akhter et al, “Document-Level Text Classification Using Single-Layer Multisize Filters Convolutional Neural Network,” IEEE Access, vol. 8, no. Ml, pp. 42689–42707, 2020, doi: 10.1109/ACCESS.2020.2976744.
A. Wahdan et al, “A systematic review of text classification research based on deep learning models in Arabic language,” Int. J. Electr. Comput. Eng., vol. 10, no. 6, pp. 6629–6643, 2020, doi: 10.11591/IJECE.V10I6.PP6629-6643.
W. Fang et al, “Automated text classification of near-misses from safety reports: An improved deep learning approach,” Adv. Eng. Informatics, vol. 44, no. March 2019, p. 101060, 2020, doi: 10.1016/j.aei.2020.101060.
T. Wang et al, “A multi-label text classification method via dynamic semantic representation model and deep neural network,” Appl. Intell., vol. 50, no. 8, pp. 2339–2351, 2020, doi: 10.1007/s10489-020-01680-w.
H. Yang et al, Deep Learning and Its Applications to Natural Language Processing. 2019.
R. Joshi et al, “Deep Learning for Hindi Text Classification: A Comparison,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, pp. 94–101. doi: 10.1007/978-3-030-44689-5_9.
A. Mohta et al, “Pre-processing and emoji classification of whatsapp chats for sentiment analysis,” in Proceedings of the 4th International Conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC 2020, 2020, pp. 514–519. doi: 10.1109/I-SMAC49090.2020.9243443.
K. Masood et al, “Semantic Analysis to Identify Students’ Feedback,” Comput. J., vol. 00, no. 00, 2020, doi: 10.1093/comjnl/bxaa130.
H. Shidek et al, “WhatsApp Chat Visualizer: A Visualization of WhatsApp Messenger’s Artifact Using the Timeline Method,” Int. J. Inf. Commun. Technol., vol. 6, no. 1, pp. 1–9, 2020, doi: 10.21108/ijoict.2020.61.489.
S. Defit et al, “Timeline of Exploration on the Best Response Time in WhatsApp Group " Our Jokes at STMIK Amik ",” Matrik J. Manajemen, Tek. Inform. dan Rekayasa, vol. 20, no. 2, p. 317~324, 2021, doi: 10.30812/matrik.v20i2.1149.
N. Sharma et al, “Machine Learning and Deep Learning Applications-A Vision,” Glob. Transitions Proc., pp. 0–8, 2021, doi: 10.1016/j.gltp.2021.01.004.
Ravishankara K et al, “Whatsapp Chat Analyzer,” Int. J. Eng. Res. Technol., vol. 9, no. 5, pp. 897–900, 2020, doi: 10.17577/ijertv9is050676.
M. Thomas and C. A. Latha, “Sentimental analysis of transliterated text in Malayalam using recurrent neural networks,” J. Ambient Intell. Humaniz. Comput., vol. 11, no. 7, 2020, doi: 10.1007/s12652-020-02305-3.
A. Sinha et al, “Text Segregation on Asynchronous Group Chat,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 1371–1380. doi: 10.1016/j.procs.2020.04.147.
S. H. S. Nizam et al, “Keyword Indexing And Searching Tool (KIST): A Tool to Assist the Forensics Analysis of WhatsApp Chat,” Int. J. Inf. Commun. Technol., vol. 6, no. 1, pp. 23–30, 2020, doi: 10.21108/ijoict.2020.61.481.
A. Ahmad and M. Abubakar, “Sentiment Analysis and Classification of Asuu Whatsapp Group Post using Data Mining,” J. Confl. Resolut. Soc. Issues, vol. 1, no. 2, pp. 17–26, 2022.
L. Dogruel and A. Schnauber-Stockmann, “What determines instant messaging communication? Examining the impact of person- and situation-level factors on IM responsiveness,” Mob. Media Commun., vol. 9, no. 2, pp. 210–228, 2020, doi: 10.1177/2050157920943926.
D. B. G, J. A, and P. K. V, “Toxic Sentiment Identification Using R Programming,” Int. J. Eng. Technol. Manag. Sci., vol. 4, no. 5, pp. 76–81, 2020, doi: 10.46647/ijetms.2020.v04i05.014.
A. Yadav et al, “Understanding the political inclination of WhatsApp chats,” in ACM International Conference Proceeding Series, 2020, pp. 361–362. doi: 10.1145/3371158.3371227.
L. Blasi et al., “Reactions and countermeasures of medical oncologists towards the incoming COVID-19 pandemic: A whatsapp messenger-based report from the Italian college of chief medical oncologists,” Ecancermedicalscience, vol. 14, pp. 1–11, 2020, doi: 10.3332/ECANCER.2020.1046.
I. Saputra et al, “Comparison of the Performance of the k-Nearest Neighbor, Naïve Bayes Classifier and Support Vector Machine Algorithm With SMOTE for Classification of Bully Behavior on the WhatsApp Messenger Application,” 2021, pp. 143–149. doi: 10.2991/assehr.k.201230.028.
T. K. Eds et al., “Beyond social chit chat? Analysing the social practice of a mobile messaging service on a higher education teacher development course,” Adv. Intell. Syst. Comput., vol. 3, no. 1, pp. 1–6, 2020, doi: 10.1177/1750481319868853.
S. Kalakala, “A Rule Based Sentiment Analysis of Whatsapp Reviews in Telugu Language,” 2021.
H. Taha Assaggaf, “A Discursive and Pragmatic Analysis of WhatsApp Text-based Status Notifications,” Arab World English J., vol. 10, no. 4, pp. 101–111, 2019, doi: 10.24093/awej/vol10no4.8.
Y. Zhou et al, “Text Sentiment Analysis Based on a New Hybrid Network Model,” Comput. Intell. Neurosci., vol. 2022, pp. 1–15, 2022.
B. S. Rintyarna et al, “Evaluating the performance of sentence level features and domain sensitive features of product reviews on supervised sentiment analysis tasks,” J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0246-8.
F. E. Ayo et al, “Machine learning techniques for hate speech classification of twitter data: State-of-The-Art, future challenges and research directions,” Comput. Sci. Rev., vol. 38, p. 100311, 2020, doi: 10.1016/j.cosrev.2020.100311.
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