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http://dx.doi.org/10.22937/IJCSNS.2021.21.9.25

Sentiment Analysis From Images - Comparative Study of SAI-G and SAI-C Models' Performances Using AutoML Vision Service from Google Cloud and Clarifai Platform  

Marcu, Daniela (Stefan cel MareUniversity of Suceava)
Danubianu, Mirela (Stefan cel MareUniversity of Suceava)
Publication Information
International Journal of Computer Science & Network Security / v.21, no.9, 2021 , pp. 179-184 More about this Journal
Abstract
In our study we performed a sentiments analysis from the images. For this purpose, we used 153 images that contain: people, animals, buildings, landscapes, cakes and objects that we divided into two categories: images that suggesting a positive or a negative emotion. In order to classify the images using the two categories, we created two models. The SAI-G model was created with Google's AutoML Vision service. The SAI-C model was created on the Clarifai platform. The data were labeled in a preprocessing stage, and for the SAI-C model we created the concepts POSITIVE (POZITIV) AND NEGATIVE (NEGATIV). In order to evaluate the performances of the two models, we used a series of evaluation metrics such as: Precision, Recall, ROC (Receiver Operating Characteristic) curve, Precision-Recall curve, Confusion Matrix, Accuracy Score and Average precision. Precision and Recall for the SAI-G model is 0.875, at a confidence threshold of 0.5, while for the SAI-C model we obtained much lower scores, respectively Precision = 0.727 and Recall = 0.571 for the same confidence threshold. The results indicate a lower classification performance of the SAI-C model compared to the SAI-G model. The exception is the value of Precision for the POSITIVE concept, which is 1,000.
Keywords
Sentiments analysis from images; convolutional neural network;
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