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 %.
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Acknowledgments
We would like to acknowledge the support of the European Commission through the project MAESTRA Learning from Massive, Incompletely annotated, and Structured Data (Grant number ICT-2013-612944).
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Stojanovski, D., Strezoski, G., Madjarov, G., Dimitrovski, I. (2015). Twitter Sentiment Analysis Using Deep Convolutional Neural Network. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_60
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DOI: https://doi.org/10.1007/978-3-319-19644-2_60
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