Develop an Automatic System based on Object Recognition Techniques for Predicting Visual Sentiments from Social Network Images | ||||
المجلة العلمية لكلية التربية النوعية جامعة دمياط | ||||
Article 5, Volume 2023, Issue 8, December 2023, Page 92-118 PDF (1.25 MB) | ||||
Document Type: المقالة الأصلية | ||||
DOI: 10.21608/sjeud.2023.235413.1012 | ||||
View on SCiNiTO | ||||
Authors | ||||
Hend Ellaban 1; Elsaeed AbdElrazek 2; Ahmed El-Harby 3; Doaa Hawa 1 | ||||
1Damietta University, Faculty of Specific Education, Computer Department | ||||
2Damietta University, Faculty of Specific Education, , Computer Department | ||||
3Damietta University, Faculty of Computers and Artificial Intelligence, Computer Science Department | ||||
Abstract | ||||
Social networks have become a vital part of everybody's life, as users on popular social networking platforms share millions of images to express their opinions and personal emotions. Therefore, images have emerged as one of the most effective methods for transmitting sentiments on social networks. This has resulted in a solid vision to analyze social network images to predict positive and negative sentiments from these images. In this paper, an automatic system based on object recognition is developed by combining InceptionV3 and Long Short-Term Memory networks for predicting visual sentiments. This system aims to recognize the salient objects from social network images and predict their sentiments. Firstly, the InceptionV3 pre-trained CNN network is fine-tuned to recognize objects from images. After that, the object features are extracted using the trained network. Finally, a Long Short-Term Memory network is used to learn sentiments from object features to predict visual sentiment. The experiment results showed that the proposed system is a more powerful system for predicting visual sentiments by combining Inception V3 and Long ShortTerm Memory networks. The proposed system achieved 98.2% for predicting visual sentiments. | ||||
Keywords | ||||
Visual sentiments; InceptionV3; LSTM; Social Network images; Object recognition | ||||
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