Multimodal Sentiment Analysis of Social Media Posts Using Deep Neural Networks

Document Type : Original Article


1 Department of Computer Eng., Shahrekord University, Shahrekord, Iran

2 Department of Computer Engineering, Faculty of Engineering Shahrekord University Shahrekord, Iran

3 Computer Engineering Dept., Shahrekord University, Shahrekord, Iran


With the fast growth of social media, they have become the most important platform for posting multimodal content generated by users. Much of the data on social networks such as Instagram and Telegram is multimodal data. With the aim of analyzing such multimodal data in social networks, multimodal sentiment analysis has become one of the most significant subjects for researchers in the field of emotion recognition and data mining. Although multimodal sentiment analysis of social media data for English language has been addressed in several researches recently, few studies addressed the problem for the Persian language which is the official language of more than 120 million of people around the word. In this study, a multimodal deep learning model is proposed to address this problem. The proposed method utilizes a bi-directional long short-term memory (bi-LSTM) for processing text posts and a VGG16 convolutional network for analyzing images. A new dataset of Instagram and Telegram posts, MPerSocial, containing 1000 pairs of images and Persian comments is introduced in the current study and used for evaluating the proposed method. The results of experiments show that using the fusion of textual and image modalities improves sentiment polarity detection accuracy by 20% and 8% compared with the scenario in which image and text modalities in isolation. Also, the performance of the proposed model is better than three similar deep and four traditional machine learning models. All codes and dataset used in the current study are publicly available at GitHub.  


Main Subjects

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 Aria Naseri Karimvand received his B.S. degree in software engineering from MJDKH university in 2019 and his M.S. from Shahrekord University in 2021. His research interest includes natural language processing, deep learning, and social media data mining.

 Shahla Nemati was born in Shiraz, Iran in 1982. She received the B.S. degree in hardware engineering from Shiraz University, Shiraz, Iran, in 2005, the M.S. degree from Isfahan University of Technology, Isfahan, Iran, in 2008, and the Ph.D. degree in computer engineering from Isfahan University, Isfahan, Iran, in 2016. Since 2017, she has been an Assistant Professor with the Computer Engineering Department, Shahrekord University, Shahrekord, Iran. Her research interests include data fusion, affective computing, and data mining.

 Reza Salehi Chegeni received his B.S. degree in software engineering from Lorestan university in 2018 and his M.S. from Shahrekord University in 2021. His research interest includes evolutionry algorithms, natural language processing, deep learning, and data mining.

 Mohammad Ehsan Basiri received the B.S. degree in software engineering from Shiraz University, Shiraz, Iran, in 2006 and the M.S. and Ph.D. degrees in Artificial Intelligence from Isfahan University, Isfahan, Iran, in 2009 and 2014. Since 2014, he has been an Assistant Professor with the Computer Engineering Department, Shahrekord University, Shahrekord, Iran. He is the author of three books and more than 35 articles. His research interests include sentiment analysis, natural language processing, deep learning, and data mining