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Combining Sentiment Analysis Scores to Improve Accuracy of Polarity Classification in MOOC Posts

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Progress in Artificial Intelligence (EPIA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11804))

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Abstract

Sentiment analysis is a set of techniques that deal with the verification of sentiment and emotions in written texts. This introductory work aims to explore the combination of scores and polarities of sentiments (positive, neutral and negative) provided by different sentiment analysis tools. The goal is to generate a final score and its respective polarity from the normalization and arithmetic average scores given by those tools that provide a minimum of reliability. The texts analyzed to test our hypotheses were obtained from forum posts from participants in a massive open online course (MOOC) offered by Universidade Aberta de Portugal, and were submitted to four online service APIs offering sentiment analysis: Amazon Comprehend, Google Natural Language, IBM Watson Natural Language Understanding, and Microsoft Text Analytics. The initial results are encouraging, suggesting that the average score is a valid way to increase the accuracy of the predictions from different sentiment analyzers.

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Notes

  1. 1.

    https://aulaberta.uab.pt/.

  2. 2.

    https://www.postgresql.org/.

  3. 3.

    https://www.knime.com.

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Author information

Authors and Affiliations

  1. Universidade Aberta de Portugal, Lisbon, Portugal

    Herbert Laroca Pinto & Vitor Rocio

  2. INESC TEC, Porto, Portugal

    Vitor Rocio

  3. LE@D, Lisbon, Portugal

    Vitor Rocio

Authors
  1. Herbert Laroca Pinto
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  2. Vitor Rocio
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Corresponding author

Correspondence to Herbert Laroca Pinto .

Editor information

Editors and Affiliations

  1. INESC-TEC, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    Paulo Moura Oliveira

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. LIACC/UP, University of Porto, Porto, Portugal

    Luís Paulo Reis

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Pinto, H.L., Rocio, V. (2019). Combining Sentiment Analysis Scores to Improve Accuracy of Polarity Classification in MOOC Posts. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_4

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  • DOI: https://doi.org/10.1007/978-3-030-30241-2_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30240-5

  • Online ISBN: 978-3-030-30241-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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