Abstract
In this chapter, we will perform sentiment analysis from text data. Generally, sentiment analysis is performed based on the processing of natural language, the analysis of text and computational linguistics. Although data can come from different data sources, in this chapter we will analyze sentiment in text data, using two particular text data examples: one from film critics, where the text is highly structured and maintains text semantics; and another example coming from social networks, where the text can show a lack of structure and users may use text abbreviations. We will review basic mechanisms required to perform sentiment analysis, including data cleaning, producing a general representation of the text, and performing some statistical inference on the text represented to determine positive and negative sentiments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Z. Ren, J. Yuan, J. Meng, Z. Zhang, IEEE Transactions on Multimedia 15(5), 1110 (2013)
A.L. Maas, R.E. Daly, P.T. Pham, D. Huang, A.Y. Ng, C. Potts, in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (Association for Computational Linguistics, Portland, Oregon, USA, 2011), pp. 142–150. URL http://www.aclweb.org/anthology/P11-1015
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. Manning, A. Ng, C. Potts, Conference on Empirical Methods in Natural Language Processing (2013)
E. Cambria, B. Schuller, Y. Xia, C. Havasi, IEEE Intelligent Systems 28(2), 15 (2013)
B. Pang, L. Lee, Found. Trends Inf. Retr. 2(1–2), 1 (2008)
Acknowledgements
This chapter was co-written by Sergio Escalera and Santi Seguí.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Igual, L., Seguí, S. (2017). Statistical Natural Language Processing for Sentiment Analysis. In: Introduction to Data Science. Undergraduate Topics in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-50017-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-50017-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50016-4
Online ISBN: 978-3-319-50017-1
eBook Packages: Computer ScienceComputer Science (R0)