Classification of Emotions on Twitter using Emotion Lexicon and Naïve Bayes

Dhiya Fairuz, Novi Yusliani, Kanda Januar Miraswan

Abstract

Social media is a means of interaction and communication. One of the social media that is often used is Twitter. Twitter allows its users to express many things, one of which is being a personal media to provide various kinds of expressions from its users such as emotions. Users can express their emotions and sentiments through writing on the status of their social media posts. One method to find out the emotion in the sentence is using the Emotion Lexicon. However, the lexicon-based method is not good at classifying data because not every word contains emotion. So, there's a need to combine it with other classification method such as Naive Bayes. Naïve Bayes relies on independent assumptions to obtain a classification through the probability hypothesis that each class has. The results of the classification test with Emotion Lexicon alone have 46% accuracy, 45% precision, 51% recall and 36% f-measure. While the results of the classification test with Emotion Lexicon and Naïve Bayes resulted in an accuracy of 65%, precision of 77%, recall of 55%, and f- measure of 59%.

Full Text:

PDF

References

M. S. Saputri, R. Mahendra, and M. Adriani, “Emotion Classification on Indonesian Twitter Dataset,”

Proc. 2018 Int. Conf. Asian Lang. Process. IALP 2018, no. November, pp. 90–95, 2019.

H. Krishnan, “Emotion Detection of Tweets using Naïve Bayes Classifier,” Int. J. Eng. Technol. Sci. Res., vol. 4, no. 11, pp. 457–462, 2017.

I. M. D. Ardiada, M. Sudarma, and D. Giriantari, “Text Mining pada Sosial Media untuk Mendeteksi Emosi Pengguna Menggunakan Metode Support Vector Machine dan K-Nearest Neighbour,” Maj. Ilm. Teknol. Elektro, vol. 18, no. 1, p. 55, 2019.

E. Safaie et al., “Emotion and Sentiment Analysis from Twitter Text,” no. 3, pp. 1–13, 2018.

J. Bata, Suyoto, and Pranowo, “Leksikon Untuk Deteksi Emosi Dari Teks Bahasa Indonesia,” Semin. Nas. Inform. 2015 (semnasIF 2015), vol. 2015, no. November, pp. 195–202, 2015.

S. M. Mohammad and P. D. Turney, “Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon,” CAAGET ’10 Proc. NAACL HLT 2010 Work. Comput. Approaches to Anal. Gener. Emot. Text, no. June, pp. 26–34, 2010.

S. M. Mohammad and P. D. Turney, “Crowdsourcing a word-emotion association lexicon,” Comput. Intell., vol. 29, no. 3, pp. 436–465, 2013.

A. F. Anees, A. Shaikh, A. Shaikh, and S. Shaikh, “Survey Paper on Sentiment Analysis : Techniques and Challenges,” EasyChair Prepr., vol. 2389, 2020.

Bustami, “Penerapan Algoritma Naive Bayes,” J. Inform., vol. 8, no. 1, pp. 884–898, 2014.

A. N. Rohman, E. Utami, and S. Raharjo, “Deteksi Kondisi Emosi pada Media Sosial Menggunakan Pendekatan Leksikon dan Natural Language Processing,” Eksplora Inform., vol. 9, no. 1, pp. 70–76, 2019.

S. Mujilahwati, “Pre-Processing Text Mining Pada Data Twitter,” Semin. Nas. Teknol. Inf. dan Komun., vol. 2016, no. Sentika, pp. 2089–9815, 2016.

E. D. Liddy, “Natural Language Processing. In Encyclopedia of Library and Information Science,”

Marcel Decker, Inc., pp. 1–15, 2001.

O. Somantri, “Text Mining Untuk Klasifikasi Kategori Cerita Pendek Menggunakan Naïve Bayes (NB),” J. Telemat., vol. 12, no. 01, 2017.

DOI: https://doi.org/10.36706/sjia.v2i2.24

Refbacks

  • There are currently no refbacks.