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Advisor(s)
Abstract(s)
Multimodal sentiment analysis is a process for the classi-
cation of the content of composite comments in social media at the
sentiment level that takes into consideration not just the textual content
but also the accompanying images. A composite comment is normally
represented by the union of text and image. Multimodal sentiment analysis
has a great dependency on text to obtain its classi cation, because
image analysis can be very subjective according to the context where
the image is inserted. In this paper we propose a method that reduces
the text analysis dependency on this kind of classi cation giving more
importance to the image content. Our method is divided into three main
parts: a text analysis method that was adapted to the task, an image
classi er tuned with the dataset that we use, and a method that analyses
the class content of an image and checks the probability that it belongs
to one of the possible classes. Finally a weighted sum takes the results of
these methods into account to classify content according to its sentiment
class. We improved the accuracy on the dataset used by more than 9%.
Description
Keywords
Multimodal Sentiment Analysis Image Text Deep Learning