<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The research examines the accuracy of current solution models for the Arabic text sentiment classification, including traditional machine learning and deep learning algorithms. The main aim is to detect the opinion and emotion expressed in Telecom companies’ customers tweets. Three supervised machine learning algorithms, Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF), and one deep learning algorithm, Convolutional Neural Network (CNN) were applied to classify the sentiment of 1098 unique Arabic textual tweets. The research results show that deep learning CNN using Word Embedding achieved higher performance in terms of accuracy with F1 score = 0.81. Furthermore, in the aspect classification task, the results reveal that applying Part of Speech (POS) features with deep learning CNN algorithm was efficient and reached 75 % accuracy using a dataset consisting of 1277 tweets. Additionally, in this study, we added an additional task of extracting the geographical location information from the tweet content. The location detection model achieved the following precision values: 0.6 and 0.89 for both Point of Interest (POI) and city (CIT).</p>
</abstract>

The research examines the accuracy of current solution models for the Arabic text sentiment classification, including traditional machine learning and deep learning algorithms. The main aim is to detect the opinion and emotion expressed in Telecom companies’ customers tweets. Three supervised machine learning algorithms, Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF), and one deep learning algorithm, Convolutional Neural Network (CNN) were applied to classify the sentiment of 1098 unique Arabic textual tweets. The research results show that deep learning CNN using Word Embedding achieved higher performance in terms of accuracy with F1 score = 0.81. Furthermore, in the aspect classification task, the results reveal that applying Part of Speech (POS) features with deep learning CNN algorithm was efficient and reached 75 % accuracy using a dataset consisting of 1277 tweets. Additionally, in this study, we added an additional task of extracting the geographical location information from the tweet content. The location detection model achieved the following precision values: 0.6 and 0.89 for both Point of Interest (POI) and city (CIT).

Aspect-based Sentiment Analysis and Location Detection for Arabic Language Tweets

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

The research examines the accuracy of current solution models for the Arabic text sentiment classification, including traditional machine learning and deep...

Aspect-based Sentiment Analysis and Location Detection for Arabic Language Tweets

Data publikacji: 24 sty 2023

Zakres stron: 119 - 127

DOI: https://doi.org/10.2478/acss-2022-0013

Słowa kluczowe
Aspect detection, Arabic language, deep learning, location detection, sentiment analysis

© 2022 Norah AlShammari et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Aspect-based Sentiment Analysis and Location Detection for Arabic Language Tweets

Data publikacji: 24 sty 2023

Zakres stron: 119 - 127

DOI: https://doi.org/10.2478/acss-2022-0013

Słowa kluczoweAspect detection, Arabic language, deep learning, location detection, sentiment analysis

© 2022 Norah AlShammari et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Aspect detection, Arabic language, deep learning, location detection, sentiment analysis