Real-Time Lexicon-Based Sentiment Analysis Experiments On Twitter With A Mild (More Information, Less Data)

Date

2017-12-14

Author

Arslan, Yusuf
Birtürk, Ayşe Nur
Djumabaev, Bekjan
Kucuk, Dilek

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Sentiment analysis of Twitter data is a well studied area, however, there is a need for exploring the effectiveness of real-time approaches on small data sets that only include popular and targeted tweets. In this paper, we have employed several sentiment analysis techniques by using dynamic dictionaries and models, and performed some experiments on limited but relevant datasets to understand the popularity of some terms and the opinion of users about them. The results of our experiments are promising.

Subject Keywords

Sentiment analysis, Social media, Data mining

URI

https://hdl.handle.net/11511/54952

Conference Name

IEEE International Conference on Big Data (IEEE Big Data)

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

Y. Arslan, A. N. Birtürk, B. Djumabaev, and D. Kucuk, “Real-Time Lexicon-Based Sentiment Analysis Experiments On Twitter With A Mild (More Information, Less Data),” presented at the IEEE International Conference on Big Data (IEEE Big Data), Boston, MA, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54952.