Twitter sentiment analysis using deep convolutional neural network
Date Issued
2015-06-22
Author(s)
Stojanovski, Dario
Strezoski, Gjorgji
Madjarov, Gjorgji
Abstract
In the work presented in this paper, we conduct experiments
on sentiment analysis in Twitter messages by using a deep convolutional
neural network. The network is trained on top of pre-trained word embeddings obtained by unsupervised learning on large text corpora. We
use CNN with multiple filters with varying window sizes on top of which
we add 2 fully connected layers with dropout and a softmax layer. Our
research shows the effectiveness of using pre-trained word vectors and
the advantage of leveraging Twitter corpora for the unsupervised learning phase. The experimental evaluation is made on benchmark datasets
provided on the SemEval 2015 competition for the Sentiment analysis
in Twitter task. Despite the fact that the presented approach does not
depend on hand-crafted features, we achieve comparable performance to
state-of-the-art methods on the Twitter2015 set, measuring F1 score of
64.85%.
on sentiment analysis in Twitter messages by using a deep convolutional
neural network. The network is trained on top of pre-trained word embeddings obtained by unsupervised learning on large text corpora. We
use CNN with multiple filters with varying window sizes on top of which
we add 2 fully connected layers with dropout and a softmax layer. Our
research shows the effectiveness of using pre-trained word vectors and
the advantage of leveraging Twitter corpora for the unsupervised learning phase. The experimental evaluation is made on benchmark datasets
provided on the SemEval 2015 competition for the Sentiment analysis
in Twitter task. Despite the fact that the presented approach does not
depend on hand-crafted features, we achieve comparable performance to
state-of-the-art methods on the Twitter2015 set, measuring F1 score of
64.85%.
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