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Sentiment Analysis of Social Media for Evaluating Universities

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Automation, Communication and Cybernetics in Science and Engineering 2015/2016

Abstract

In the age of digitalization, a huge amount of sentiments are expressed daily on university related topics using social media platforms. Particularly, posted statements from students and teachers can provide a potential source for evaluating universities. Twitter as one of the most popular microblogging platforms is a rich data resource for opinion mining. Stimulated by this fact, ways to analyze Twitter for information in the context of universities are sought. This paper looks at the analysis of social media sentiment as a complementary source for evaluating universities. The extracted results can support university rankings that experience criticism in terms of measuring vital indicators. This paper relays on sentiment analysis methods to analyze opinions published on Twitter. For this purpose, at first, tweets that are related to selected universities in Germany were collected. Second, the tweets were classified based on their sentiment into “Positive” and “Not Positive” tweets. At last, the results were analyzed providing information about the communicative topics at the universities. This paper gives an outlook to further research in context of an automated analysis of social media content in order to support the evaluation of universities.

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Notes

  1. 1.

    TU9 is an incorporated society of the nine most prestigious, oldest, and largest universities focusing on engineering and technology in Germany.

  2. 2.

    TU: Technical University.

  3. 3.

    API: Application Programming Interface.

  4. 4.

    Few tweets belong to more than one university, that leads to have the tweets summation larger than the total count of collected tweets.

  5. 5.

    OMG: Oh My God!.

  6. 6.

    WTH: What the hell!.

  7. 7.

    DKDC: Don’t know, don’t care.

  8. 8.

    TY: Thank you.

  9. 9.

    Patriotic Europeans Against the Islamisation of the West.

  10. 10.

    Congratulations.

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Authors and Affiliations

  1. IMA/ZLW & IfU, RWTH Aachen University, Dennewartstr. 27, 52068, Aachen, Germany

    Anas Abdelrazeq, Daniela Janßen, Christian Tummel, Sabina Jeschke & Anja Richert

Authors
  1. Anas Abdelrazeq
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  2. Daniela Janßen
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  3. Christian Tummel
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  4. Sabina Jeschke
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  5. Anja Richert
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Corresponding author

Correspondence to Anas Abdelrazeq .

Editor information

Editors and Affiliations

  1. IMA/ZLW, RWTH Aachen Univ IMA/ZLW, Aachen, Germany

    Sabina Jeschke

  2. Faculty of Mechanical Engineering, IMA/ZLW & IfU - RWTH Aachen Univers Faculty of Mechanical Engineering, Aachen, Nordrhein-Westfalen, Germany

    Ingrid Isenhardt

  3. Faculty of Mechanical Engineering, IMA/ZLW & IfU - RWTH Aachen Univers Faculty of Mechanical Engineering, Aachen, Germany

    Frank Hees

  4. Faculty of Mechanical Engineering, IMA/ZLW & IfU - RWTH Aachen Univers Faculty of Mechanical Engineering, Aachen, Germany

    Klaus Henning

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Abdelrazeq, A., Janßen, D., Tummel, C., Jeschke, S., Richert, A. (2016). Sentiment Analysis of Social Media for Evaluating Universities. In: Jeschke, S., Isenhardt, I., Hees, F., Henning, K. (eds) Automation, Communication and Cybernetics in Science and Engineering 2015/2016. Springer, Cham. https://doi.org/10.1007/978-3-319-42620-4_19

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