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In today’s connected world, social media produces a daily stream of data that allows businesses and researchers to better understand users’ opinions and sentiments. However, effectively using this data for sentiment analysis poses challenges in a language that includes both standard and dialectal variations. Our study aims to overcome these obstacles by applying a probabilistic Naive Bayes model in combination with Term Frequency-Inverse Document Frequency techniques to analyze sentiments expressed in texts written in Modern Standard Arabic and Moroccan Colloquial Arabic. The results of our study demonstrate the effectiveness of this approach in extracting actionable insights from textual data.

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Sentiment Analysis Based on Multinomial Naive Bayes Classifier for Standard Arabic and Moroccan Dialect

  • 会议论文

  • 发表日期：

    2026年

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  • Chorouk Ferroud,
  • Abdeljalil Elouardighi

摘要

In today’s connected world, social media produces a daily stream of data that allows businesses and researchers to better understand users’ opinions and sentiments. However, effectively using this data for sentiment analysis poses challenges in a language that includes both standard and dialectal variations. Our study aims to overcome these obstacles by applying a probabilistic Naive Bayes model in combination with Term Frequency-Inverse Document Frequency techniques to analyze sentiments expressed in texts written in Modern Standard Arabic and Moroccan Colloquial Arabic. The results of our study demonstrate the effectiveness of this approach in extracting actionable insights from textual data.