Opinion Analysis for Emotional Classification on Emoji Tweets using the Naïve Bayes Algorithm

Siti Sendari, Ilham Ari Elbaith Zaeni, Dian Candra Lestari, Hanny Prasetya Hariyadi

Abstract


Opinion Analysis is a research study needed to social media, since the content could become a trending topic and has a significant impact on social life. One of the social media that have a big contribution to cyberspace and information development is Twitter. In the Twitter application, users can insert images that represent emotions, facial expressions, or icons. Emoji is a graphic symbol in the form of an image to express a thing, with the Emoji, a text can be read and understood according to its meaning because the image represents it. Of the several things that have been mentioned then, the researchers conducted research on the classification of tweet content based on the use of Emojis. This study aims to determine the emotional uses of Twitter in one period. Every tweet on the Twitter timeline, which contains both text and Emojis, will be classified according to several categories. The algorithm used was Naïve Bayes. It calculated the probability of Emoji tweet to obtain the text classification with Emojis. The results of the classification of emotions are grouped with three categories, namely "angry," "joy," and "sad," it showed that the category "joy" had become the emotional trend of Twitter users where Emojis (x1f60a) dominate the most. Meanwhile, the accuracy of the algorithm used to reach 90% with a 70:30 holdout technique.

Full Text:

PDF

References


N. Alias, M. S. Sabdan, K. A. Aziz, M. Mohammed, I. S. Hamidon, and N. Jomhari, "Research Trends and Issues in the Studies of Twitter: A Content Analysis of Publications in Selected Journals (2007 – 2012)," Procedia - Soc. Behav. Sci., vol. 103, pp. 773–780, 2013.

A. Uhl, N. Kolleck, and E. Schiebel, "Twitter data analysis as contribution to strategic foresight-The case of the EU Research Project' Foresight and Modelling for European Health Policy and Regulations' (FRESHER)," Eur. J. Futur. Res., vol. 5, no. 1, 2017.

Statista Research Department, "Twitter: number of users in Indonesia 2019 | Statista," 2019. [Online]. Available: https://www.statista.com/statistics/490591/twitter-users-malaysia/.

P. K. Novak, J. Smailović, B. Sluban, and I. Mozetič, “Sentiment of emojis,” PLoS One, vol. 10, no. 12, pp. 1–21, 2015.

Y. Tang and K. F. Hew, "Emoticon, emoji, and sticker use in computer-mediated communication: A review of theories and research findings," Int. J. Commun., vol. 13, pp. 2457–2483, 2019.

H. Miller, D. Kluver, J. Thebault-Spieker, L. Terveen, and B. Hecht, "Understanding emoji ambiguity in context: The role of text in emoji-related miscommunication," Proc. 11th Int. Conf. Web Soc. Media, ICWSM 2017, pp. 152–161, 2017.

D. Bandorski et al., "Contraindications for video capsule endoscopy," World J. Gastroenterol., vol. 22, no. 45, pp. 9898–9908, 2016.

E. Vyvyan, The Emoji Code: The Linguistics Behind Smiley Faces and Scaredy Cats Handbook, 2017.

I. Ileri and P. Karagoz, "Detecting user emotions in twitter through collective classification," IC3K 2016 - Proc. 8th Int. Jt. Conf. Knowl. Discov. Knowl. Eng. Knowl. Manag., vol. 1, no. Ic3k, pp. 205–212, 2016.

M. S. Asriadie, M. S. Mubarok, and Adiwijaya, "Classifying emotion in Twitter using Bayesian network," in Journal of Physics: Conference Series, 2018, vol. 971, no. 1.

F. Hallsmar and J. Palm, "Multi-class Sentiment Classification on Twitter using an Emoji Training Heuristic," pp. 1–27, 2016.

S. Narr, M. Hulfenhaus, and S. Albayrak, "Language-independent Twitter sentiment analysis," Knowl. Discov. Mach. Learn. (KDML), LWA, pp. 12–14, 2012.

F. Barbieri et al., “SemEval 2018 Task 2: Multilingual Emoji Prediction,” pp. 24–33, 2018.

H. W. Raj and S. Balachandran, “Future Emoji Entry Prediction Using Neural Networks,” Journal of Computer Science, vol. 16, no. 2, pp. 150–157, Feb. 2020

J. Berengueres and D. Castro, "Differences in emoji sentiment perception between readers and writers," Proc. - 2017 IEEE Int. Conf. Big Data, Big Data 2017, vol. 2018-Janua, pp. 4321–4328, 2018.

S. Lau, "The effect of smiling on person perception," J. Soc. Psychol., vol. 117, no. 1, pp. 63–67, 1982.

J. Berengueres and D. Castro, "Sentiment Perception of Readers and Writers in Emoji use," 2017.

G. Guibon, M. Ochs, and P. Bellot, "From Emojis to Sentiment Analysis," 2016.

S. Ayvaz and M. O. Shiha, "The Effects of Emoji in Sentiment Analysis," Int. J. Comput. Electr. Eng., vol. 9, no. 1, pp. 360–369, 2017.

S. Khalil and M. Fakir, "RCrawler: An R package for parallel web crawling and scraping," SoftwareX, vol. 6, pp. 98–106, 2017.

S. Sendari et al. / Knowledge Engineering and Data Science 2020, 3 (1): 50–59 59

M. Desai and M. A. Mehta, "Techniques for sentiment analysis of Twitter data: A comprehensive survey," Proceeding - IEEE Int. Conf. Comput. Commun. Autom. ICCCA 2016, no. April 2016, pp. 149–154, 2017.

A. S. Raamkumar, M. Erdt, H. Vijayakumar, E. Rasmussen, and Y. L. Theng, "Understanding the Twitter usage of humanities and social sciences academic journals," Proc. Assoc. Inf. Sci. Technol., vol. 55, no. 1, pp. 430–439, 2018.

V. A. and S. S. Sonawane, "Sentiment Analysis of Twitter Data: A Survey of Techniques," Int. J. Comput. Appl., vol. 139, no. 11, pp. 5–15, 2016.

J. K. and J. R., "Stop-Word Removal Algorithm and its Implementation for Sanskrit Language," Int. J. Comput. Appl., vol. 150, no. 2, pp. 15–17, 2016.

M. Adriani, J. Asian, B. Nazief, S. M. M. Tahaghoghi, and H. E. Williams, “Stemming Indonesian,” ACM Transactions on Asian Language Information Processing, vol. 6, no. 4, pp. 1–33, Dec. 2007

H. Pajupuu, R. Altrov, and J. Pajupuu, “Identifying Polarity in Different Text Types,” Folklore: Electronic Journal of Folklore, vol. 64, pp. 125–142, Jun. 2016

G. Yurtalan, M. Koyuncu, and Ç. Turhan, "A polarity calculation approach for lexicon-based Turkish sentiment analysis," Turkish J. Electr. Eng. Comput. Sci., vol. 27, no. 2, pp. 1325–1339, 2019.

F. C. Permana, Y. Rosmansyah, and A. S. Abdullah, “Naive Bayes as opinion classifier to evaluate students satisfaction based on student sentiment in Twitter Social Media,” Journal of Physics: Conference Series, vol. 893, p. 012051, Oct. 2017.

E. Hauthal, D. Burghardt, and A. Dunkel, "Analyzing and visualizing emotional reactions expressed by emojis in location-based social media," ISPRS Int. J. Geo-Information, vol. 8, no. 3, 2019.

Li-guo Duan, D. Peng, and Ai-ping Li, “A New Naive Bayes Text Classification Algorithm,” TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 12, no. 2, Feb. 2014

M. S. Saputri, R. Mahendra, and M. Adriani, "Emotion Classification on Indonesian Twitter Dataset Emotion Classification on Indonesian Twitter Dataset," in International Conference on Asian Language Processing, 2018, no. November.

B. Tessem, S. Bjørnestad, W. Chen, and L. Nyre, “Word cloud visualisation of locative information,” J. Locat. Based Serv., vol. 9, no. 4, pp. 254–272, 2015.




DOI: http://dx.doi.org/10.17977/um018v3i12020p50-59

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Knowledge Engineering and Data Science

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Flag Counter

Creative Commons License


This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

View My Stats