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Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Natural Language Processing
  • Sentiment Analysis

Background:

  • Social media use has increased, making posts valuable data for sentiment analysis.
  • Turkish language presents unique challenges for sentiment analysis due to its agglutinative nature.

Purpose of the Study:

  • To perform sentiment analysis on Turkish Twitter data using machine learning algorithms.
  • To assess the impact of the pandemic on public opinion through sentiment analysis.
  • To create a benchmark dataset for Turkish sentiment analysis.

Main Methods:

  • Utilized Natural Language Processing techniques within a machine learning framework.
  • Applied several machine learning algorithms to analyze Turkish tweets.
  • Created a custom dataset, SentimentSet, by manually marking tweets related to the pandemic.

Main Results:

  • Achieved classification accuracy up to approximately 87% on test data from both public and custom datasets.
  • Demonstrated classification accuracy up to approximately 84% on a smaller, custom-generated test dataset.
  • The results highlight language-specific sentiment analysis for Turkish.

Conclusions:

  • Machine learning algorithms can effectively perform sentiment analysis on Turkish social media data.
  • The developed SentimentSet dataset can serve as a benchmark for future research.
  • This study contributes to understanding public opinion and language-specific sentiment analysis in Turkish.