A novel COVID‐19 sentiment analysis in Turkish based on the combination of convolutional neural network and bidirectional long‐short term memory on Twitter

Abstract

The whole world has been experiencing the COVID‐19 pandemic since December 2019. During the pandemic, a new life has been started by necessity where people have extensively used social media to express their feelings, and find information. Twitter was used as the source of what people have shared regarding the COVID‐19 pandemic. Sentiment analysis deals with the extraction of the sentiment of a given text. Most of the related works deal with sentiment analysis in English, while studies for Turkish sentiment analysis lack in the research field. To this end, a novel sentiment analysis model based on the combination of convolutional neural network and bidirectional long short‐term memory was proposed in this study. The proposed deep neural network model was trained on the constructed Twitter dataset, which consists of 15k Turkish tweets regarding the COVID‐19 pandemic, to classify a given tweet into three sentiment classes, namely, (i) positive, (ii) negative, and (iii) neutral. A set of experiments were conducted for the evaluation of the proposed model. According to the experimental result, the proposed model obtained an accuracy as high as 97.895%, which outperformed the state‐of‐the‐art baseline models for sentiment analysis of tweets in Turkish.