Utilizing Deep Learning in Arabic Text Classification Sentiment Analysis of Twitter

Abstract

The number of social media users has increased. These users share and reshare their ideas in posts and this information can be mined and used by decision-makers in different domains, who analyse and study user opinions on social media networks to improve the quality of products or study specific phenomena. During the COVID-19 pandemic, social media was used to make decisions to limit the spread of the disease using sentiment analysis. Substantial research on this topic has been done; however, there are limited Arabic textual resources on social media. This has resulted in fewer quality sentiment analyses on Arabic texts. This study proposes a model for Arabic sentiment analysis using a Twitter dataset and deep learning models with Arabic word embedding. It uses the supervised deep learning algorithms on the proposed dataset. The dataset contains 51,000 tweets, of which 8,820 are classified as positive, 37,360 neutral, and 8,820 as negative. After cleaning it will contain 31,413. The experiment has been carried out by applying the deep learning models, Convolutional Neural Network and Long Short-Term Memory while comparing the results of different machine learning techniques such as Naive Bayes and Support Vector Machine. The accuracy of the AraBERT model is 0.92% when applying the test on 3,505 tweets.

A Novel Approach: Tokenization Framework based on Sentence Structure in Indonesian Language

“…3. 1) Word Tokenization, is a tokenization process as commonly used, breaking text data into words [8] [34]. If there are punctuation marks then they will be attached to this token.…”

“…One of the activities in pre-processing is tokenization besides case folding, filtering/stop-words removal, lemmatization, stemming [5], [6] including normalization and removing irrelevant words [7]. Stopwords are the least important words in a sentence, and ignoring them can help identify the most important words [8].…”