DETEKSI EVENT PADA HASHTAG TWITTER MENGGUNAKAN DF-IDF DAN ENTROPI WAVELET
-
Amin Ajaib Maggang(1*)
Universitas Nusa Cendana
(*) Corresponding Author
Abstract
This study aims to detect event from a collection of tweets having same hashtag. Discrete Wavelet Transform (DWT) and Document Frequency-Inverse Document Frequency (DF-IDF) techniques were utilised in this research to develop and analyse the signal. Signals are developed using DF-IDF during a certain period while DWT is applied to capture the sudden change in the DF-IDF signal and display it in the form of entropy. Words that have a sudden change in signal value at the same period of time might represent an event related to the topic of the hashtag. Knowing the words will assist users to discover the event associated to the hashtag. Using 3473 tweets collected on the RIPLKY hashtag with the 20 most frequently occurring words, the results showed that DWT managed to capture the sudden change in df-idf signal related to a spike occured in the wavelet entropi. The spike was shown by three words, namely proud, sophialimx, and goals. We also found that the three words were under the same tweet, and it describes an event about the funeral of Singaporean prime minister.
Downloads
References
Statista. (2020, 26 Juni 2020). Number of Twitter users worldwide from 2014 to 2020(in millions). Available: https://www.statista.com/statistics/303681/twitter-users-worldwide/
R. Kusumawardani and M. Basri, "Topic Identification and Categorization of Public Information in Community-Based Social Media," in Journal of Physics: Conference Series, 2017, p. 012075.
M. Cordeiro, "Twitter event detection: combining wavelet analysis and topic inference summarization," in Doctoral symposium on informatics engineering, 2012, pp. 11-16.
J. Weng and B.-S. Lee, "Event detection in twitter," in Fifth international AAAI conference on weblogs and social media, 2011.
W. Dou, X. Wang, W. Ribarsky, and M. Zhou, "Event detection in social media data," in IEEE VisWeek Workshop on Interactive Visual Text Analytics-Task Driven Analytics of Social Media Content, 2012, pp. 971-980.
S. Petrović, M. Osborne, and V. Lavrenko, "Streaming first story detection with application to twitter," in Human language technologies: The 2010 annual conference of the north american chapter of the association for computational linguistics, 2010, pp. 181-189.
Q. He, K. Chang, and E.-P. Lim, "Analyzing feature trajectories for event detection," in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007, pp. 207-214.
O. A. Rosso, S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schürmann, et al., "Wavelet entropy: a new tool for analysis of short duration brain electrical signals," Journal of neuroscience methods, vol. 105, pp. 65-75, 2001.
C. E. Shannon, "A mathematical theory of communication," ACM SIGMOBILE mobile computing and communications review, vol. 5, pp. 3-55, 2001.
S. J. Orfanidis, Optimum signal processing: an introduction: Macmillan publishing company, 1988.
This work is licensed under CC BY-SA 4.0
