  In recent years, the growing ubiquity of Internet memes on social media
platforms, such as Facebook, Instagram, and Twitter, has become a topic of
immense interest. However, the classification and recognition of memes is much
more complicated than that of social text since it involves visual cues and
language understanding. To address this issue, this paper proposed a
parallel-channel model to process the textual and visual information in memes
and then analyze the sentiment polarity of memes. In the shared task of
identifying and categorizing memes, we preprocess the dataset according to the
language behaviors on social media. Then, we adapt and fine-tune the
Bidirectional Encoder Representations from Transformers (BERT), and two types
of convolutional neural network models (CNNs) were used to extract the features
from the pictures. We applied an ensemble model that combined the BiLSTM,
BIGRU, and Attention models to perform cross domain suggestion mining. The
officially released results show that our system performs better than the
baseline algorithm. Our team won nineteenth place in subtask A (Sentiment
Classification). The code of this paper is availabled at :
https://github.com/YuanLi95/Semveal2020-Task8-emotion-analysis.
