Multimodal Sentiment Analysis Based on Deep Learning Methods Such as Convolutional Neural Networks

Dear Colleagues,

This Special Issue is aimed at researchers who are working on large scale data with real-word problem solving. Every social media app is generating data in GBs that require researcher attention for identifying useful patterns and information. Due to globalization, every social media content is shared and commented on by diverse background users. The data contain several opinions in different languages on the similar topics. The classical approach for text classification, i.e., sentiment analysis, mainly relies on NLP techniques related to single language. However, it is important to propose a model that can learn features from multilingual data. Submissions on this issue focused on theoretical expansion and also the applications of sentiment analysis on our daily life are invited. Topics of interest include but are not limited to the following areas:

• Text classification;
• Opinion mining;
• Visualization of opinions;
• Social network analysis for sentiment analysis;
• Multi-model learning for text classification;
• Multi-lingual sentiment analysis;
• Applications for sentiment analysis;
• Explainable artificial intelligence for sentiment analysis;
• Aspect-based sentiment analysis;
• Hate speech detection;
• Sarcasm and irony detection.

Dr. Junaid Baber
Dr. Ali Shariq Imran
Prof. Dr. Doudpota Sher
Dr. Maheen Bakhtyar 
Topic Editors