Automatic Visual Sentiment Analysis with Convolution Neural network
Todaychr('39')s digital world demands about automated sentiment analysis on visual and text content to significantly displaying peoplechr('39')s feelings, opinions and emotions through text, images and videos across popular social networks. Earlier visual sentimental analysis fac...
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Veröffentlicht in: | International journal of industrial engineering & production research 2020-09, Vol.31 (3), p.351-360 |
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Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
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Online-Zugang: | Volltext |
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Zusammenfassung: | Todaychr('39')s digital world demands about automated sentiment analysis on visual and text content to significantly displaying peoplechr('39')s feelings, opinions and emotions through text, images and videos across popular social networks. Earlier visual sentimental analysis faces many drawbacks like achieve low accuracy and more difficult to understand people opinions due to traditional techniques. Also, another major challenge is a huge number of images generated and uploaded every day across the world. This paper overcomes problems of visual sentiment analysis with the help of deep learning convolution neural network (CNN) and Affective Regions approach to achieve more meaningful sentiment reports with huge accuracy. |
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ISSN: | 2008-4889 2345-363X |