Advertisement

Sentiment Analysis

A Students Point of View
  • Hofer DominikEmail author
Conference paper

Zusammenfassung

Sentiment Analysis (SA) is a new, fast growing scientific field, which makes it quite difficult for people, e.g.: marketing executives, sociologists, etc. to stay up to date to the vast possibilities, this field offers. But also for students, who are interested in learning a subject, apart from university, this task can be quite demanding. Due to technological advancements, it is easy to gain knowledge about aspects of SA, but it still takes time to experiment and analyze various techniques. Therefore, in this presentation, there will be an overview of the different approaches of SA, and how some of them can be applied. This includes the coding language Python, libraries/toolkits, and the involvement of social media. The primary goal is to give an overview of existing possibilities of SA implementations.

Schlüsselwörter

sentiment analysis probability level toolkit libraries 

Literatur

  1. [1] J. Pustejovsky, A. Stubbs, “The Basics,” Natural Language Annotation for Machine Learning,, first edition. Sebastopol: O’Reilly, 2013, Chap. 1, pp. 1 – 31.Google Scholar
  2. [2] Lexalytics. (2017, June 30). Sentiment Analysis: What is Sentiment Analysis [Online]. Website, Access: https://www.lexalytics.com/technology/sentiment
  3. [3] B. Liu, “The Problem of Sentiment Analysis,” Sentiment Analysis: Mining Opinions, Sentiments, and Emotions, 1st edition. New York: Cambridge University Press, 2015, Chap 2, pp. 17 – 45.Google Scholar
  4. [4] Sciencedirect. (2017, June 30). Sentiment Analysis Algorithms and Applications: A Survey [Online]. Website, Access: http://www.sciencedirect.com/science/article/pii /S2090447914000550
  5. [5] Nadeau, D., Turney, P.D., and Matwin, S. (2006), Unsupervised namedentity recognition: Generating gazetteers and resolving ambiguity, Proceedings of the 19th Canadian Conference on Artificial Intelligence (CAI-06), Quebec City, Canada, pp. 266-277.Google Scholar
  6. [6] Saedsayad. (2017, June 30). Naïve Bayesian [Online]. Website, Access: http://www.saedsayad.com/naive_bayesian.htm
  7. [7] Rationalistramble (2017, June 30). Bayesian Networks [Online]. Websie, Access: https://rationalistramble.wordpress.com/2015/12/09/bayesian-networks/
  8. [8] NLTK (2017, June 30). Natural Language Toolkit [Online]. Website, Access: www.nltk.org.
  9. [9] Textblob (2017, 30 June). TextBlob: Simplified Text Processing [Online]. Website, Access: textblob.readthedocs.io/en/dev/index.html.Google Scholar
  10. [10] WEKA University of Waikato (2017, June 30). Weka 3: Data Mining Software in Java [Online]. Website, Access: http://www.cs.waikato.ac.nz/ml/weka/index.html
  11. [11] IBM Data Scientist Workbench (2017, June 30). Data Scientist Workbench [Online]. Access: https://datascientistworkbench.com

Copyright information

© Springer Fachmedien Wiesbaden GmbH 2017

Authors and Affiliations

  1. 1.Informationstechnologie und SystemmanagementSalzburg University of Applied SciencesSalzburgÖsterreich

Personalised recommendations