Abstract
Sentiment analysis is a popular research topic in social media analysis and natural language processing. In this paper, we present the details and evaluation results of our Twitter sentiment analysis experiments which are based on word embeddings vectors such as word2vec and doc2vec, using an ANN classifier. In these experiments, we utilized two publicly available sentiment analysis datasets and four smaller datasets derived from these datasets, in addition to a publicly available trained vector model over 400 million tweets. The evaluation results are accompanied with discussions and future research directions based on the current study. One of the main conclusions drawn from the experiments is that filtering out the emoticons in the tweets could be a facilitating factor for sentiment analysis on tweets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/. Software available from tensorflow.org
Arslan, Y., Birturk, A., Djumabaev, B., Küçük, D.: Real-time lexicon-based sentiment analysis experiments on Twitter with a mild (more information, less data) approach. In: 2017 IEEE International Conference on Big Data, BigData 2017, pp. 1892–1897 (2017)
Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, vol. 10, pp. 2200–2204 (2010)
Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. arXiv preprint arXiv:1511.08308 (2015)
Chollet, F.: Keras (2015). https://github.com/fchollet/keras
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford 1(12) (2009)
Godin, F., Vandersmissen, B., De Neve, W., Van de Walle, R.: Multimedia Lab \(@\) ACL WNUT NER Shared Task: named entity recognition for Twitter microposts using distributed word representations. In: Workshop on Noisy User-generated Text (WNUT), pp. 146–153 (2015)
Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space Odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2017)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)
Lilleberg, J., Zhu, Y., Zhang, Y.: Support vector machines and word2vec for text classification with semantic features. In: IEEE 14th International Conference on Cognitive Informatics and Cognitive Computing (ICCI* CC), pp. 136–140 (2015)
Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data, pp. 415–463. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-3223-4_13
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Naji, L.: Twitter sentiment analysis training corpus (dataset) (2017). http://thinknook.com/twitter-sentiment-analysis-training-corpusdataset-2012-09-22/. Accessed 06 Feb 2016
Pang, B., Lee, L., et al.: Opinion mining and sentiment analysis. Found. Trends® Inf. Retr. 2(1–2), 1–135 (2008)
Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50. ELRA, Valletta, Malta (May 2010). http://is.muni.cz/publication/884893/en
dos Santos, C., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: 25th International Conference on Computational Linguistics (COLING), pp. 69–78 (2014)
Sharma, A., Dey, S.: A document-level sentiment analysis approach using artificial neural network and sentiment lexicons. ACM SIGAPP Appl. Comput. Rev. 12(4), 67–75 (2012)
Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for Twitter sentiment classification. In: 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1555–1565 (2014)
Xue, B., Fu, C., Shaobin, Z.: A study on sentiment computing and classification of Sina Weibo with word2vec. In: IEEE International Congress on Big Data (BigData Congress), pp. 358–363 (2014)
Zhang, D., Xu, H., Su, Z., Xu, Y.: Chinese comments sentiment classification based on word2vec and SVMperf. Expert Syst. Appl. 42(4), 1857–1863 (2015)
Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis: a survey. arXiv preprint arXiv:1801.07883 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Arslan, Y., Küçük, D., Birturk, A. (2018). Twitter Sentiment Analysis Experiments Using Word Embeddings on Datasets of Various Scales. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-91947-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91946-1
Online ISBN: 978-3-319-91947-8
eBook Packages: Computer ScienceComputer Science (R0)