Arabic Sentiment Analysis: A Systematic Literature Review

Ghallab, Abdullatif; Mohsen, Abdulqader M.; Ali, Yousef

doi:10.1155/2020/7403128

Cited by 61 publications

(51 citation statements)

References 89 publications

(116 reference statements)

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“…Feature selection (i.e., what the model receives as input) is another step toward building an accurate and generalizable model. N-Grams (i.e., the sequence of n-words) is an established featuring engineering technique in computational linguistics that is found to be popular and effective in Arabic semantic analysis applications (Ghallab, Mohsen, & Ali, 2020;Silva, Goncalves, & Cunha, 2016). This approach allows for models to learn both the individual words and the semantic relationship between multiple words.…”

Section: Methodsmentioning

confidence: 99%

“…These models allow for incorporating the uncertainty stemming out of the prior dataset distribution in contrast to the global or posterior distribution. In this proposal, Naï ve Bayes was utilized as it is the most commonly probabilistic supervised learning classification model used in Arabic sentiment analysis (Ghallab et al, 2020). The aforementioned Arabic sentiment dataset (Kaggle, 2019) was split into 47,000 and 11,000 Tweets for training and testing, respectively.…”

Section: Methodsmentioning

confidence: 99%

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Sentiment Analysis of Tweets in Saudi Arabia Regarding Governmental Preventive Measures to Contain COVID-19

Mohammed¹,

Khalifah²,

Aljubran³

et al. 2020

Preprint

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Background: Countries around the world are facing extraordinary challenges in implementing various measures to slow down the spread of the novel coronavirus (COVID-19). Guided by international recommendations, Saudi Arabia has implemented a series of infection control measures after the detection of the first confirmed case in the country. However, in order for these measures to be effective, public attitudes and compliance must be conducive as perceived risk is strongly associated with health behaviors. The primary objective of this study is to assess Saudis’ attitudes towards COVID-19 preventive measures to guide future health communication content. Methods: Naïve Bayes machine learning model was used to run Arabic sentiment analysis of Twitter posts through the Natural Language Toolkit (NLTK) library in Python. Tweets containing hashtags pertaining to seven public health measures imposed by the government were collected and analyzed. Results: A total of 53,127 tweets were analyzed. All measures, except one, showed more positive tweets than negative. Measures that pertain to religious practices showed the most positive sentiment. Discussion: Saudi Twitter users showed support and positive attitudes towards the infection control measures to combat COVID-19. It is postulated that this conducive public response is reflective of the overarching, longstanding popular confidence in the government. Religious notions may also play a positive role in preparing believers at times of crises. Findings of this study broadened our understanding to develop proper public health messages and promote stronger compliance with control measures to control COVID-19.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Sentiment Analysis of Tweets in Saudi Arabia Regarding Governmental Preventive Measures to Contain COVID-19

Mohammed¹,

Khalifah²,

Aljubran³

et al. 2020

Preprint

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“…Norah Fahad Alshammari [19] Sentiment analysis classification for twitter data from Arabic to English with deep learning model. A complete review of Arabic sentiment analysis is given by Abdullatif Ghallab [20].…”

mentioning

confidence: 99%

Multilingual twitter sentiment analysis using machine learning

Kodirekka

Srinagesh²

2020

IJECE

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Twitter sentiment analysis is one of the leading research fields. Most of the researchers were contributed to twitter sentiment analysis in English tweets, but few researchers focus on the multilingual twitter sentiment analysis. Some challenges are hoping for the research solutions in multilingual twitter sentiment analysis. This study presents the implementation of sentiment analysis in multilingual twitter data and improves the data classification up to the adequate level of accuracy. Twitter is the sixth leading social networking site in the world. Active users for twitter in a month are 330 million. People can tweet or re-tweet in their languages and allow users to use emoji’s, abbreviations, contraction words, miss spellings, and shortcut words. The best platform for sentiment analysis is twitter. Multilingual tweets and data sparsity are the two main challenges. In this paper, the MLTSA algorithm gives the solution for these two challenges. MLTSA algorithm divides into two parts. One is detecting and translating non-English tweets into English using natural language processing (NLP). And the second one is an appropriate pre-processing method with NLP support can reduce the data sparsity. The result of the MLTSA with SVM achieves good accuracy by up to 95%.

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“…Therefore, the complexity of analyzing tweets arises from the dialect differences and use of the new phrases among the young people. Also, since the Arabic language is spoken by different people from different countries and cultures (14) , there might be two different opinions about a person or an event based on the dialect that is used on the tweet even they use the same words. So, we might know the sentiment based on that.…”

Section: Data Collection and Preparation 21 Twitter Datamentioning

confidence: 99%

“…We explored a very helpful review paper about Arabic Sentiment Analysis ASA (14) , and we found couple studies that show a variance in the goals and results. That variance explained the difficulties in sentiment analysis using Arabic text, even the reviewed papers had applied different algorithms in different data sets.…”

Section: Introductionmentioning

confidence: 99%

Bag-of-Phrases (BoPh) and sentiment analysis of Arabic text in Twitter

Alshammari¹

2020

IJST

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Background/Objectives: Sentiment analysis plays main role in various text mining problems. Although, the Arabic text mining is important especially in the field of sentiment analysis, there is a paucity of research in it, especially, when it plays an important role in different issues in Arabic countries. Arabic language has many dialects that people use to express their feelings in social media. The objective of this study is to perform an experiment that follow the subjective opinion from the text. Subjective Analysis is one way that we can implement to improve the accuracy of the sentiment results in such texts in some dialects, that hide various meanings behind the words such as Saudi dialect. Methods/Statistical analysis: In this study, we manually annotated more than 8,000 tweets to have training and testing data sets with positive or negative words and phrases. Then we proposed a “Bag of Phrases” methodology to analyze the sentiments in the texts, which helped to improve the performance of sentiment analysis. Since using bag of words method is not enough in many cases, we applied a Naive Bayes algorithm to test our method. Findings: The results show that the accuracy of having True positive or True negative is about 84% comparing by using manual annotation process. The accuracy is calculated after taking into consideration the margin of error due to the manual annotation step and subjective interpretation of the texts by the annotators. Novelty/Applications: The novelty of the study is having more accurate training data set comparing with the other works in Saudi dialect for Arabic text, and proposing the BoPh concept.

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Arabic Sentiment Analysis: A Systematic Literature Review

Cited by 61 publications

References 89 publications

Sentiment Analysis of Tweets in Saudi Arabia Regarding Governmental Preventive Measures to Contain COVID-19

Sentiment Analysis of Tweets in Saudi Arabia Regarding Governmental Preventive Measures to Contain COVID-19

Multilingual twitter sentiment analysis using machine learning

Bag-of-Phrases (BoPh) and sentiment analysis of Arabic text in Twitter

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