  Sentiment analysis is attracting more and more attentions and has become a
very hot research topic due to its potential applications in personalized
recommendation, opinion mining, etc. Most of the existing methods are based on
either textual or visual data and can not achieve satisfactory results, as it
is very hard to extract sufficient information from only one single modality
data. Inspired by the observation that there exists strong semantic correlation
between visual and textual data in social medias, we propose an end-to-end deep
fusion convolutional neural network to jointly learn textual and visual
sentiment representations from training examples. The two modality information
are fused together in a pooling layer and fed into fully-connected layers to
predict the sentiment polarity. We evaluate the proposed approach on two widely
used data sets. Results show that our method achieves promising result compared
with the state-of-the-art methods which clearly demonstrate its competency.

Computation and Language;Computer Vision and Pattern Recognition;Information Retrieval