Sentiment Analysis of Social Networks Messages

Author:

Tretyakov Evgeny,Savić Dobrica,Korpusenko Anastasia,Ionkina Kristina

Publisher

Springer International Publishing

Reference12 articles.

1. Barbieri, F., Saggion, H., Ronzano, F.: Modelling sarcasm in twitter a novel approach. In: Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 50–58 (2014)

2. Bharti, S.K., Vachza, B., Pradhan, R.K., Babu, K.S., Jena, S.K.: Sarcastic sentiment detection in tweets streamed in real time: a big data approach. Digit. Commun. Netw. 2, 108–121 (2016)

3. Davidov, D., Tsur, O., Rappoport, A.: Semi-supervised recognition of sarcastic sentences in twitter and amazon. In: Proceedings of the Fourteenth Conference on Computational Natural Language Learning, ACL, pp. 107–116 (2010)

4. Digital Ocean [Online source]. Shaumik Daityari: How To Perform Sentiment Analysis in Python 3 Using the Natural Language Toolkit (NLTK) (2019). https://www.digitalocean.com/community/tutorials/how-to-perform-sentiment-analysis-in-python-3-using-the-natural-language-toolkit-nltk. Accessed 04 Jun 2020

5. Go, A., Bhayani, R., Huang, L.: Twitter Sentiment Classification Using Distant Supervision, p. 138. Technical report, Stanford (2009)

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