  Sentiment analysis is a research topic focused on analysing data to extract
information related to the sentiment that it causes. Applications of sentiment
analysis are wide, ranging from recommendation systems, and marketing to
customer satisfaction. Recent approaches evaluate textual content using Machine
Learning techniques that are trained over large corpora. However, as social
media grown, other data types emerged in large quantities, such as images.
Sentiment analysis in images has shown to be a valuable complement to textual
data since it enables the inference of the underlying message polarity by
creating context and connections. Multimodal sentiment analysis approaches
intend to leverage information of both textual and image content to perform an
evaluation. Despite recent advances, current solutions still flounder in
combining both image and textual information to classify social media data,
mainly due to subjectivity, inter-class homogeneity and fusion data
differences. In this paper, we propose a method that combines both textual and
image individual sentiment analysis into a final fused classification based on
AutoML, that performs a random search to find the best model. Our method
achieved state-of-the-art performance in the B-T4SA dataset, with 95.19%
accuracy.
