| Summary: | Often a picture is worth a thousand words, and this is a small statement that represents one of the biggest challenges in the Image Sentiment Analysis area. The main theme of this dissertation is the Image Sentiment Analysis of social media, mainly from Twitter, so that it is identified as situations that represent risks (identification of negative situations) or that become a risk (prediction of negative situations). Despite the diversity of work done in the area of image sentiment analysis, it is still a challenging task. Several factors contribute to the difficulty, both more global factors likewise sociocultural issues, and issues within the scope of the analysis of feeling in images, such as the difficulty in finding reliable and properly labeled data to be used, as well as factors faced during the classification, for example, it is normal to associate images with darker colors and low brightness to negative feelings, after all, most are like that, but some cases escape this rule, and it is these cases that affect the accuracy of the developed models. However, in order to overcome these problems faced in classification, a multitasking model was developed, which will consider the entire image information, information from the salient areas in the images, and the facial expressions of faces contained in the images, and textual information, so that each component complements the other during classification. During the experiments it was possible to observe that the use of the proposed models can bring advantages for the classification of feeling in images and even work around some problems evidenced in existing works, such as the irony of the text. Therefore, this work aims to present the state of the art and the study carried out, in order to enable the presentation and implementation of the proposed model and carrying out the experiments and discussion of the results obtained, in order to verify the effectiveness of what was proposed. Finally, conclusions about the work done and future work will be presented.
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