Emotion Analysis from Turkish Tweets Using Deep Neural Networks

27Citations
Citations of this article
79Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Text data analysis of social media is becoming more and more important since it includes the most recent information on what people think about. Likewise, emotion is one of the most valuable parts of human communication, emotion analysis is a type of information extraction process which identifies the emotional states of a given text. In this study, we investigated the performance of deep neural networks on emotion analysis from Turkish tweets. For this, we examined three different deep learning architectures including artificial neural network (ANN), convolutional neural network (CNN) and recurrent neural network (RNN) with long short-Term memory (LSTM). Besides, we curated a dataset of Turkish tweets and annotated each tweet automatically for six emotion categories using a lexicon-based approach. For the evaluation, we conducted a set of experiments for each architecture. The results showed that the lexicon-based automatic annotation of tweets is valid. Secondly, ANN produced the worst result as expected, and CNN resulted in the highest score of 0.74 in terms of accuracy measure. Experiments also showed that our proposed approach for emotion analysis of tweets in Turkish performs better than state-of-The-Art in this topic.

Cite

CITATION STYLE

APA

Tocoglu, M. A., Ozturkmenoglu, O., & Alpkocak, A. (2019). Emotion Analysis from Turkish Tweets Using Deep Neural Networks. IEEE Access, 7, 183061–183069. https://doi.org/10.1109/ACCESS.2019.2960113

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free