![](bg7.png)
Figure 3: Confusion Matrix.
ated on a benchmark of tweets, obtaining results that
outperform the state-of-the-art models. We conducted
some experiments to compare LSTM with GRU, and
we found that the results are similar. We finally de-
cided to use GRU because it has a number of param-
eters lower than LSTM. The confusion matrix of the
results obtained by TD-biGRU shows that most of the
misclassified examples are related to the neutral cate-
gory. In the future work we plan to extend our model
to handle this weakness by integrating more informa-
tion such as lexicon information and/or the depen-
dency tree.
ACKNOWLEDGEMENTS
The authors acknolwedge the support of Univ. Rovira
i Virgili through a Mart
´
ı i Franqu
´
es PhD grant and the
Research Support Funds 2015PFR-URV-B2-60.
REFERENCES
Bengio, Y., Ducharme, R., Vincent, P., and Jauvin, C.
(2003). A neural probabilistic language model. jour-
nal of machine learning research, 3(Feb):1137–1155.
Bengio, Y., Simard, P., and Frasconi, P. (1994). Learning
long-term dependencies with gradient descent is diffi-
cult. IEEE transactions on neural networks, 5(2):157–
166.
Cho, K., Van Merri
¨
enboer, B., Gulcehre, C., Bahdanau, D.,
Bougares, F., Schwenk, H., and Bengio, Y. (2014).
Learning phrase representations using rnn encoder-
decoder for statistical machine translation. arXiv
preprint arXiv:1406.1078.
Chollet, F. (2015). Keras. https://github.com/fchollet/keras.
Deriu, J., Gonzenbach, M., Uzdilli, F., Lucchi, A., De Luca,
V., and Jaggi, M. (2016). Swisscheese at semeval-
2016 task 4: Sentiment classification using an ensem-
ble of convolutional neural networks with distant su-
pervision. Proceedings of SemEval, pages 1124–1128.
Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., and Xu, K.
(2014). Adaptive recursive neural network for target-
dependent twitter sentiment classification. In ACL (2),
pages 49–54.
Feldman, R. (2013). Techniques and applications for
sentiment analysis. Communications of the ACM,
56(4):82–89.
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I.,
and Salakhutdinov, R. R. (2012). Improving neural
networks by preventing co-adaptation of feature de-
tectors. arXiv preprint arXiv:1207.0580.
Jabreel, M. and Moreno, A. (2016). Sentirich: Sentiment
analysis of tweets based on a rich set of features.
In Artificial Intelligence Research and Development
- Proceedings of the 19th International Conference
of the Catalan Association for Artificial Intelligence,
Barcelona, Catalonia, Spain, October 19-21, 2016,
pages 137–146.
Jiang, L., Yu, M., Zhou, M., Liu, X., and Zhao, T. (2011).
Target-dependent twitter sentiment classification. In
Proceedings of the 49th Annual Meeting of the Asso-
ciation for Computational Linguistics: Human Lan-
guage Technologies-Volume 1, pages 151–160. Asso-
ciation for Computational Linguistics.
Kim, Y. (2014). Convolutional neural networks for sentence
classification. arXiv preprint arXiv:1408.5882.
Liu, B. (2011). Opinion mining and sentiment analysis. In
Web Data Mining, pages 459–526. Springer.
Liu, B. (2012). Sentiment analysis and opinion mining.
Synthesis lectures on human language technologies,
5(1):1–167.
Liu, P., Joty, S., and Meng, H. (2015). Fine-grained opin-
ion mining with recurrent neural networks and word
embeddings. In Conference on Empirical Methods in
Natural Language Processing (EMNLP 2015).
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013).
Efficient estimation of word representations in vector
space. arXiv preprint arXiv:1301.3781.
Mohammad, S., Kiritchenko, S., and Zhu, X. (2013). NRC-
Canada: Building the State-of-the-Art in Sentiment
Analysis of Tweets. In Proceedings of the seventh
international workshop on Semantic Evaluation Ex-
ercises (SemEval-2013), Atlanta, Georgia, USA.
Pang, B. and Lee, L. (2008). Opinion Mining and Sentiment
Analysis. Found. Trends Inf. Retr., 2(1-2):1–135.
Pang, B., Lee, L., and Vaithyanathan, S. (2002). Thumbs
Up?: Sentiment Classification Using Machine Learn-
ing Techniques. In Proceedings of the ACL-02 Con-
ference on Empirical Methods in Natural Language
Processing - Volume 10, EMNLP ’02, pages 79–
86, Stroudsburg, PA, USA. Association for Compu-
tational Linguistics.
Pennington, J., Socher, R., and Manning, C. D. (2014).
Glove: Global vectors for word representation. In
Empirical Methods in Natural Language Processing
(EMNLP), pages 1532–1543.
Severyn, A. and Moschitti, A. (2015). UNITN: Training
deep convolutional neural network for Twitter sen-
timent classification. In Proceedings of the 9th In-
WEBIST 2017 - 13th International Conference on Web Information Systems and Technologies
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