![](bga.png)
international conference on machine learning (ICML-
11), pages 513–520.
Graves, A. (2013). Generating sequences with recurrent
neural networks. arXiv preprint arXiv:1308.0850.
Hochreiter, S. (1998). The vanishing gradient problem du-
ring learning recurrent neural nets and problem solu-
tions. International Journal of Uncertainty, Fuzziness
and Knowledge-Based Systems, 6(02):107–116.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term
memory. Neural computation, 9(8):1735–1780.
Hu, M. and Liu, B. (2004). Mining and summarizing cu-
stomer reviews. In Proceedings of the tenth ACM
SIGKDD international conference on Knowledge dis-
covery and data mining, pages 168–177. ACM.
Kim, Y. (2014). Convolutional neural networks for sentence
classification. CoRR, abs/1408.5882.
Kouloumpis, E., Wilson, T., and Moore, J. D. (2011). Twit-
ter sentiment analysis: The good the bad and the omg!
Icwsm, 11(538-541):164.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998).
Gradient-based learning applied to document recogni-
tion. Proceedings of the IEEE, 86(11):2278–2324.
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J., Be-
thard, S. J., and McClosky, D. (2014). The Stanford
CoreNLP natural language processing toolkit. In As-
sociation for Computational Linguistics (ACL) System
Demonstrations, pages 55–60.
Masnadi-Shirazi, H. and Vasconcelos, N. (2009). On the
design of loss functions for classification: theory, ro-
bustness to outliers, and savageboost. In Advances in
neural information processing systems, pages 1049–
1056.
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. M. and Turney, P. D. (2013). Crowdsour-
cing a word-emotion association lexicon. 29(3):436–
465.
Ortigosa, A., Mart
´
ın, J. M., and Carro, R. M. (2014). Sen-
timent analysis in facebook and its application to e-
learning. Computers in Human Behavior, 31:527–
541.
Pak, A. and Paroubek, P. (2010). Twitter as a corpus for
sentiment analysis and opinion mining. In LREc, vo-
lume 10.
Pang, B., Lee, L., and Vaithyanathan, S. (2002). Thumbs
up?: sentiment classification using machine learning
techniques. In Proceedings of the ACL-02 con-
ference on Empirical methods in natural language
processing-Volume 10, pages 79–86. Association for
Computational 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.
Pool, C. and Nissim, M. (2016). Distant supervision for
emotion detection using facebook reactions. arXiv
preprint arXiv:1611.02988.
Saif, H., He, Y., and Alani, H. (2012). Semantic sentiment
analysis of twitter. The Semantic Web–ISWC 2012,
pages 508–524.
Salton, G. and Buckley, C. (1988). Term-weighting appro-
aches in automatic text retrieval. Information proces-
sing & management, 24(5):513–523.
Sanjeev Arora, Yingyu Liang, T. M. (2017). A simple but
tough-to-beat baseline for sentence embeddings.
Sarlan, A., Nadam, C., and Basri, S. (2014). Twitter sen-
timent analysis. In Information Technology and Mul-
timedia (ICIMU), 2014 International Conference on,
pages 212–216. IEEE.
Singh, T. and Kumari, M. (2016). Role of text pre-
processing in twitter sentiment analysis. Procedia
Computer Science, 89:549–554.
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning,
C. D., Ng, A., and Potts, C. (2013). Recursive deep
models for semantic compositionality over a senti-
ment treebank. In Proceedings of the 2013 conference
on empirical methods in natural language processing,
pages 1631–1642.
Tian, Y., Galery, T., Dulcinati, G., Molimpakis, E., and Sun,
C. (2017). Facebook sentiment: Reactions and emojis.
SocialNLP 2017, page 11.
Troussas, C., Virvou, M., Espinosa, K. J., Llaguno, K., and
Caro, J. (2013). Sentiment analysis of facebook statu-
ses using naive bayes classifier for language learning.
In Information, Intelligence, Systems and Applicati-
ons (IISA), 2013 Fourth International Conference on,
pages 1–6. IEEE.
Wang, G., Sun, J., Ma, J., Xu, K., and Gu, J. (2014). Senti-
ment classification: The contribution of ensemble le-
arning. Decision support systems, 57:77–93.
Wang, X., Wei, F., Liu, X., Zhou, M., and Zhang, M. (2011).
Topic sentiment analysis in twitter: a graph-based
hashtag sentiment classification approach. In Procee-
dings of the 20th ACM international conference on In-
formation and knowledge management, pages 1031–
1040. ACM.
Wen, S. and Wan, X. (2014). Emotion classification in mi-
croblog texts using class sequential rules. In AAAI,
pages 187–193.
Yang, C., Lin, K. H.-Y., and Chen, H.-H. (2007). Emotion
classification using web blog corpora. In Web Intelli-
gence, IEEE/WIC/ACM International Conference on,
pages 275–278. IEEE.
Yang, Z. and Fang, X. (2004). Online service quality di-
mensions and their relationships with satisfaction: A
content analysis of customer reviews of securities bro-
kerage services. International Journal of Service In-
dustry Management, 15(3):302–326.
ICAART 2018 - 10th International Conference on Agents and Artificial Intelligence
220