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A new approach applying Sentiment Analysis in Greek Language

Published: 22 February 2022 Publication History

Abstract

In recent years, observing the rapid development of web applications, we are leading to the result of a huge volume of web texts that has come from forums, social media and blogs. These applications are a means of communication, commentary, criticism, presentation of proposals but also of political and religious beliefs. All these texts, small or large, capture the feelings, desires, intentions, but also the perceptions on various issues of the users, with the result that this data is valuable. In this article we present an approach for the analysis of text data in the Greek language in order to extract important information about the author's emotion. This approach classifies user-generated comments into the appropriate polarity category using basic classifications methods.

References

[1]
N. Spatiotis, I. Perikos, M. Paraskevas and I. Mporas, "Improving Sentiment Analysis for the Greek Language using Discretization Technique," Patra, 2019.
[2]
N. Spatiotis, I. Perikos, I. Mporas and M. Paraskevas, "Sentiment Analysis of Teachers Using Social Information in Educational Platform Environments," International Journal on Artificial Intelligence Tools, 2020.
[3]
O. R. Llombart, "Using Machine Learning Techniques for Sentiment Analysis," in School of Engineering (UAB), 2017.
[4]
M. Chaudhari and S. Govilkar, "A Survey of Machine Learning Techniques for Sentiment Classification," International Journal on Computational Science & Applications (IJCSA) Vol.5, vol. No.3, June 2015.
[5]
A. Yoss, "GetOldTweets3. How to bypass limitations of the Twitter Official API when scraping twitter data," 2020.

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          PCI '21: Proceedings of the 25th Pan-Hellenic Conference on Informatics
          November 2021
          499 pages
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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 22 February 2022

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          Author Tags

          1. Annotation
          2. Data preprocessing
          3. Feature vectors
          4. Machine Learning
          5. Opinion Mining
          6. Sentiment analysis

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