Amazing technological breakthrough possible @S-Logix

Office Address

  • #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
  • +91- 81240 01111

Social List

Opinion Analysis for Emotional Classification on Emoji Tweets using the Naive Bayes Algorithm - 2020

Opinion Analysis For Emotional Classification On Emoji Tweets Using The Naive Bayes Algorithm

Research Area:  Machine Learning


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.


Author(s) Name:  Siti Sendari, Ilham Ari Elbaith Zaeni, Dian Candra Lestari, Hanny Prasetya Hariyadi

Journal name:  Knowledge Engineering and Data Science (KEDS)

Conferrence name:  

Publisher name:  KEDS

DOI:  10.17977/um018v3i12020p50-59

Volume Information:  Vol 3, No 1, July 2020, pp. 50–59