article · International Journal of Advanced Computer Science and Applications
Online social media platforms host significant public discussion where individuals express opinions about products, events, and broad topics. Analysing this text allows organisations to assess sentiment and adapt strategies accordingly. However, processing dialectal content requires specialised approaches. A workflow was developed to classify sentiments expressed in Twitter comments written in Moroccan Dialectal Arabic and Modern Standard Arabic. The process involved collecting and preparing social media comments, followed by testing various feature construction techniques, including n-gram extraction, bag-of-words, term frequency-inverse document frequency, and word embeddings. Multiple classification models were assessed, specifically Naive Bayes, Random Forests, Support Vector Machines, Logistic Regression, and Long Short-Term Memory networks. Among these algorithms, the Support Vector Machine achieved the highest classification performance, reaching an accuracy level just under 70 percent.
Social media has become a primary channel for public communication and opinion sharing. Understanding sentiment in regional dialects helps governments and businesses determine what people think about specific products, policies, or events. Establishing automated tools for Moroccan Dialectal Arabic supports better public communication monitoring and allows organisations to refine their strategies using direct feedback from online discussions.
This work indicates potential applications in sentiment detection and opinion mining tools for businesses and governments wanting to evaluate consumer or public sentiment. Because testing relied on social media data and the top classification accuracy remained just below 70 percent, the technology represents early-stage research that requires further refinement and validation before being suitable for commercial deployment or reliable automated decision-making.
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As technology continues to evolve, humans tend to follow suit, and currently social media has taken place as the defacto method of communication. As it tends to happen with verbal communication, people express their opinions in written form and through an analysis of their words, one can extract what an individual wants from a product, a topic, or an event. By looking at the emotions expressed in such content, governments, businesses, and people can learn a lot that can help them improve their strategies. Therefore, in this study, we will use different algorithms to improve the Moroccan sentiment classification. The first step is to gather and prepare Moroccan Dialectal Arabic Twitter comments. Then, a lot of different combinations of extraction (n-grams) and weighting schemes (BOW/ TF-IDF) and word embedding for feature construction are applied to get the best classification models. We used Naive Bayes, Random Forests, Support Vector Machines, and Logistic regression and LSTM to classify the data we prepared. Our machine learning approach, which incorporates sentiment analysis, was designed to analyze Twitter comments written in Modern Standard Arabic or Moroccan Dialectal Arabic. As a final benchmark of our paper, we were simply a sliver shy away from the 70% mark in our accuracy by relying on the SVM algorithm. Although not a game-changing result, this was enough to encourage us to continue developing our model further.
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DOI: 10.14569/ijacsa.2023.0140347
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