Political Arabic Articles Orientation Using Rough Set Theory With Sentiment Lexicon

Alwan, Jwan K.; Hussain, Abir Jaafar; Hamed, Dhafar; Sadiq, Ahmed T.; Khalaf, Mohamed; Liatsis, Panos

doi:10.1109/access.2021.3054919

Cited by 18 publications

(5 citation statements)

References 40 publications

(41 reference statements)

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“…The results showed that using TF with TF-IDF improved the accuracy to a good ratio. In [17], The researchers conducted artificial neural network-based emotion classification using a dataset of 2,122 sentences from 206 Arabic documents. They also employed this method to categorize people's opinion posts into three groups: conservative, revolutionary, and reform.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Different Stemming Techniques on Arabic Customer Reviews

Hawraa Fadhil Khelil,

Mohammed Fadhil Ibrahim,

Hafsa Ataallah Hussein

et al. 2024

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Customer opinion and reviews play a vital role in marketing expansion. Big companies all over the world assign a lot of their efforts to analyzing customers’ feedback to keep track of their needs. Natural Language Processing (NLP) is widely used to analyze such review texts. Arabic customer analysis and classification also began to gain researchers’ attention due to the wide range of Arabic language speakers. Working with Arabic Language is a very challenging task because of the orthographic nature of Arabic. Also, customers often write their reviews in their dialectical style, which often diverts from standard Arabic. This study presents a method to classify Arabic customer reviews using four classifiers (K-nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (RL), and Naïve Bayes (NB)). The classification is implemented with three stemming techniques (Snowball, Khoja, and Tashaphyne). The HARD dataset is adopted to perform the experiments. The results stated that the stemming methods can enhance classification performance despite the complexity of Arabic scripts and dialects. This work sheds light on utilizing and investigating more machine learning (ML) techniques and evaluating the results.

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Section: Related Workmentioning

confidence: 99%

Evaluation of Different Stemming Techniques on Arabic Customer Reviews

Hawraa Fadhil Khelil,

Mohammed Fadhil Ibrahim,

Hafsa Ataallah Hussein

et al. 2024

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“…RST play a good role in SA via several languages in several applications such as political articles orientation [25] and political polarity detection [26], these researches using English and Arabic lexicon with RST. RST-based give a good results in Arabic/English sentiment analysis text.…”

Section: Rough Set Theory (Rst)mentioning

confidence: 99%

Sentiment Analysis Techniques –Survey

Mirzh,

Zuhair Hussein Ali

2023

wjps

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Online social media platforms allow people to express their ideas and opinions about anything as products, education, or politics, this produce a huge data requires appropriate tools to analyze it accurately, and that’s why Sentiment Analysis (SA) has become more used in recent years. This paper review the SA and classification methods of machine learning especially supervised techniques like Support Vector Machine (SVM), Neural Networks (NN), Naïve Bayes (NB), Rough Set Theory (RST), Random Forest (RF), Decision Trees (DT), and other technology of SA is lexicon techniques and hybrid methods (which include the lexicon and machine learning techniques). Machine learning consist of two group supervised (Classification and Regression) and unsupervised (clustering and association). The SA Affected from one system to another based on its polarity as “positive” or “negative”, and the language of data that processed.

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“…SVM is used extensively for ASA in the literature, as in [18,40,56]. In this work, SVM with sigmoid kernel was used according to the following equations [57]:…”

Section: Support Vector Machinementioning

confidence: 99%

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

Hadwan¹,

Al-Hagery²,

Al-Sarem³

et al. 2022

Computers, Materials &Amp; Continua

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Different types of pandemics that have appeared from time to time have changed many aspects of daily life. Some governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during lockdown. The Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store. A huge number of reviews are written daily by users to express their opinions, which include significant information to improve these applications. The manual processing and extracting of information from users' reviews is an extremely difficult and time-consuming task. Therefore, the use of intelligent methods is necessary to analyse users' reviews and extract issues that can help in improving these apps. This research aims to support the efforts made by the Saudi government for its citizens and residents by analysing the opinions of people in Saudi Arabia that can be found as reviews on Google Play and the app store using sentiment analysis and machine learning methods. To the best of our knowledge, this is the first study to explore users' opinions about governmental apps in Saudi Arabia. The findings of this analysis will help government officers make the right decisions to improve the quality of the provided services and help application developers improve these applications by fixing potential issues that cannot be identified during application testing phases. A new dataset used for this research includes 8000 user reviews gathered from social media, Google Play and the app store. Different methods are applied to the dataset, and the results show that the k nearest neighbourhood (KNN) method generates the highest accuracy compared to other implemented methods.

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Political Arabic Articles Orientation Using Rough Set Theory With Sentiment Lexicon

Cited by 18 publications

References 40 publications

Evaluation of Different Stemming Techniques on Arabic Customer Reviews

Evaluation of Different Stemming Techniques on Arabic Customer Reviews

Sentiment Analysis Techniques –Survey

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

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