A deep learning-based sentiment analysis approach (MF-CNN-BILSTM) and topic modeling of tweets related to the Ukraine–Russia conflict

Abstract

Twitter, one of the most significant social media platforms, can be used as data sources to research public opinion on various topics, including political conflicts. People worldwide have expressed their opinions about the war between Russia and Ukraine since it began. The motivation of this study is to use deep learning techniques to reveal qualitative and quantitatively narrow-scoped situational awareness and emotional tendencies during crisis periods. In order to achieve this, a sizable dataset of geotagged tweets was gathered with specific terms associated with the conflict between Ukraine and Russia. Then, deep learning-based text mining techniques like topic modeling and sentiment analysis were applied to investigate people’s perspectives on conflict and emotional tendencies. The study will establish an impartial data source for the reports and articles of unbiased press members. This study used Valence Aware Dictionary and sEntiment Reasoner (VADER) to categorize the emotions related to the conflict between Russia and Ukraine in tweets. In addition, Latent Dirichlet Allocation (LDA) was used to extract various discussion topics. These techniques reveal the role of Twitter and compare and analyze the emotions and attitudes expressed on Twitter by different countries during the Ukraine–Russia conflict. In addition, a new deep learning-based sentiment classification “MF-CNN-BiLSTM” model that predicts and analyzes sentiments was proposed in this study. The proposed model stands for Multistage Feature Extraction using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM). By combining various qualities and benefits of CNNs and BiLSTM, the suggested model makes it possible to identify both short- and long-term dependencies in ordinal data. The proposed model includes MF steps that strengthen feature extraction by identifying local dependencies. Experimental results demonstrate that it is possible to obtain more accurate sentiment classification results by the proposed method.