Sentiment Analysis of Twitter Users Using Deep Learning Models
Rasha Majid Hassoon
Sentiment Analysis of Twitter Users Using Deep Learning Models
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http://doi.org/10.37648/ijiest.v12i01.008
Abstract
This research suggests a robust and systematic way for Arabic Sentiment Analysis using a vast dataset of 66,666 text reviews. One of the main advantages of this study is that the dataset was perfectly balanced (33,333 positive samples and 33,333 negative samples). In machine learning, this 50/50 split is important because it eliminates class bias and enables the predictive model to treat both sentiment classes equally. As shown in the values of the metrics — overall accuracy, weighted precision, weighted recall, and F1 score — there is great similarity among them, indicating a stable and reliable assessment of the model's real potential throughout the Arabic dataset. Based on data profile, the average word count per review is 42.37 words, which is sufficient for classification of text using linguistic context.
Keywords: Sentiment Analysis; Arabic Natural Language Processing (NLP); TF-IDF; LinearSVC; Machine Learning; Twitter Data.
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