In recent years, machine learning and websites have developed rapidly. This resulted in continual and explosive growth in the sharing of ideas and views on products and services over the worldwide web in an array of s...
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ISBN:
(纸本)9783031791635;9783031791642
In recent years, machine learning and websites have developed rapidly. This resulted in continual and explosive growth in the sharing of ideas and views on products and services over the worldwide web in an array of sectors. As a result, there is an enormous flow of internet data attainable for analytical research. Sentiment analysis (SA) is a part of Natural Language Processing (NLP) that requires to process enormous amounts of data in order to identify people's opinions and sentiments. Several studies have been conducted to deal with the negative effects of social networks. This field of research is increasing popularity in both the public and private sectors, leading to the creation of several challenges. However, the majority of the available datasets were in English. Whereas the Arabic Moroccan dialect (Darija) ones were not. Following that, we created models combining NLP and Marching learning techniques to detect and classify sentiments. We evaluated the models using the most used metrics: accuracy, loss, F1-score, precision, and recall. The results of the experiment revealed modest scores between 87% and 89%. These findings imply that the models require to be upgraded due to a lack of accessible datasets and pre-processing techniques to handle the Moroccan dialect of Arabic (Darija).
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