A COMPREHENSIVE DATA ANALYSIS ON FUDMA ASUU WHATSAPP GROUP CHAT

  • Abubakar Ahmad Department of Computer Science & IT Federal University Dutsin-ma
  • Abdulhafiz A. Nuhu
  • Abdulkadir Abubakar
Keywords: Data Mining, Sentiment Analysis, WhatsApp, Emoji, Python libraries

Abstract

WhatsApp is an instant messaging application for information exchange in real time. It is a medium for communication and interaction among individuals, groups, institutions and business partners. Enormous amount of information is generated by WhatsApp in velocity, volume and variety which can serves as a source for various analyses, prediction and for other purposes. In this paper, dataset was collected from WhatsApp Group Chat, FUDMA ASUU MATTERS (FAM), a chat group of lecturers from Academic Staff Union of University (ASUU), Federal University Dutsin-Ma, Katsina state Nigeria. The primary goal is to present detailed analysis of the WhatsApp group chat to ascertain the level of involvement and participation by members in the group. Detailed analysis of fact such as the number of messages sent in different format, the most active date and time as well as the most active user(s) is to be investigated. Text classification method with Python and Jupyter notebook was used. The Python libraries applied include, Numpy, Pandas, Matplotlib and Seaborn. The result has shown that the level of participation of members compared to top ten members is by far uneven as only the top ten members accounted for more than half of the cumulative messages sent over a period of fourteen months. The research encourages members to be actively involved instead of allowing few members to dominate the platform. It is better to be an active contributor rather than remaining as a passive onlooker

References

Ahmad, A., Mukhtar, A., Akinyemi, O. O. (2021, JANUARY). Sentiment Analysis and Classification of ASUU WhatsApp Group Post Using Data Mining. JOURNAL OF CONFLICT RESOLUTION AND SOCIAL ISSUES, VOL. 1 (NO. 2), 18 - 27.

Bhattacharjee, U., Srijith, P. K. & Maunendra, D. (2019). Term Specific TF-IDF Boosting for Detection of Rumours in Social Networks. In D. o. Engineering (Ed.), In Proceedings of the Sixth Social Networking Workshop, SN@COMSNETS 2019, Bengaluru,, 116 , pp. 10–19. IIT Hydrabad India.

Cambria, E., Yang, L., Xing, Z. F., et al. (2020). SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis. CIKM, (pp. 105-114). Ireland.

Court, D. (2015). Marketing & Sales Big Data, Analytics, and the Future of Marketing & Sales. McKinsey & Company.

Darwich, M., Mohd, N., Shahrul, A., Omar, N., & Osman, N. (2019). Corpus-Based Techniques for Sentiment Lexicon Generation: A Review. Journal of Digital Information Management., 17, 296. 10.6025/jdim/2019/17/5/296-305.

Diana, M. & Adam, F. (2011). Automatic detection of political opinions in tweets. Proceedings of the 8th international conference on the semantic web,European Semantic Web Conference (ESWC’11) (pp. 88–99). Springer.

Harshal, K., Kalyani, G. & Tanmay, S. (2018, March 03). A review on: Sentiment polarity analysis on Twitter data from different Events. International Research Journal of Engineering and Technology (IRJET), 05 (03 | Mar-2018), Page 1479.

Kontopoulos, E., Berberidis, C., Dergiades, T. & Bassiliades, N. (2013). Ontology-based sentiment analysis of twitter posts. Expert Systems with Applications, 40(10), 4065-4074.

Li, S. & Tsai, F. (2011). Noise control in document classification based on fuzzy formal concept analysis. In: Presented at the IEEE. International Conference on Fuzzy Systems (FUZZ). IEEE.

Liu, B. (2010.). Sentiment Analysis and Subjectivity. In Handbook of Natural Language Processing, Second Edition. Taylor and Francis Group, Boc.

Mohanavalli, S., Karthika, S., Srividya, K.R., & Uthayan, N. S. (2018). Categorisation of Tweets Using Ensemble Classification Methods. nternational Journal of Engineering & Technology, 7 (3.12), 722-725.

Mudinas, A., Zhang, D. & Levene, M. (2012). Combining lexicon and learning based approaches for concept-level sentiment analysis Presented at the. WISDOM’12. Beijing, China.

Neumann, G. (2006). A Hybrid Machine Learning Approach for Information Extraction from Free Text. From Data and Information Analysis to Knowledge Engineering (pp. 390 - 397). Springer, Berlin, Heidelberg.

Oxford. (2019). Oxford online dictionary. accessed, 12:53PM,16th, October 2019: https://www.lexico.com/en/definition/sentiment_analysis.

Paridhi, P. N., Dinesh D. P. & Yogesh, S. P. (2018). Sentiment Classification of Twitter Data: A Review. International Research Journal of Engineering and Technology (IRJET).05, pp. 929 - 931. p-ISSN: 2395-0072: ISO 9001:2008 Certified Journal.

Patil, S. (2016). WhatsApp Group Data Analysis with R. International ournal of Computer Applications, 0975 – 8887.
Walaa, M., Ahmed, H. & Hoda, K. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5, 1093 - 1113.
Published
2021-07-01
How to Cite
Ahmad, A., Nuhu, A. A., & Abubakar, A. (2021). A COMPREHENSIVE DATA ANALYSIS ON FUDMA ASUU WHATSAPP GROUP CHAT. FUDMA JOURNAL OF SCIENCES, 5(2), 26 - 33. https://doi.org/10.33003/fjs-2021-0502-519