Sentiment Analysis of Arabic Tweets: Opinion Target Extraction

Behdenna, Salima; Barigou, Fatiha; Belalem, Ghalem

doi:10.6025/jdim/2018/16/6/324-331

Cited by 4 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Results show that the proposed approach outperformed the baseline approach, and the overall enhancement is around 53% for the first task, around 59% for the second task, and around 19% for the third one. Behdenna et al [8] proposed a machine learning approach to extract opinion target from Arabic tweets. Several feature forms are examined to evaluate their impact on the performance of extracting opinion targets using two classifiers, SVM and Naïve Bayes.…”

Section: Related Workmentioning

confidence: 99%

“…This research focuses on ABSA for the standard Arabic Language. The motivation of this choice is double-handed: on the one hand, both document level and sentence level do not find what exactly people liked and didn't like [8]. On the other hand, for opinions to be exhaustive, analysis should be supplied for each aspect or feature of the entity [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Ontology-Based Approach to Enhance Explicit Aspect Extraction in Standard Arabic Reviews

Behdenna¹,

Belalem²,

Barigou³

2022

IJCDS

Self Cite

View full text Add to dashboard Cite

Currently, one of the most important and difficult areas of research is Arabic Sentiment analysis. Aspect extraction is the most important task in aspect-based sentiment analysis. In this work, we propose an aspect-based sentiment analysis approach for Arabic language. The basic idea of the proposed approach is to use the description logics to identify explicit aspects as well as their polarities. This approach was evaluated using the publicly available human annotated Arabic dataset (HAAD) based on Semantic Evaluation 2014 workshop (SemEval-2014) along with its baseline experiments conducted by HAAD providers. Experimental results showed that the proposed approach significantly improves the performance compared with baseline approach. The F-measure was improved by 19% and 55% for the aspect terms extraction and aspect term polarity tasks respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Ontology-Based Approach to Enhance Explicit Aspect Extraction in Standard Arabic Reviews

Behdenna¹,

Belalem²,

Barigou³

2022

IJCDS

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, while most follow almost the same techniques, results and findings differ massively. For instance, after collecting data using TAGS (2019) and Behdenna et al (2018) pre-processed tweets by tokenizing the whole text into separate words, removing unwanted words – such as special characters, URLs and “RT” and deleting stop words which are words that are frequent in a corpus but do not change the meaning if removed (Al-Shalabi et al , 2004).…”

Section: The Rise Of Arabic On Social Mediamentioning

confidence: 99%

Mining Arabic Twitter conversations on health care: a new approach to analysing Arabic language on social media

Alqtati

Wilson

Silva

2021

JIMA

View full text Add to dashboard Cite

Purpose This paper aims to equip professionals and researchers in the fields of advertising, branding, public relations, marketing communications, social media analytics and marketing with a simple, effective and dynamic means of evaluating consumer behavioural sentiments and engagement through Arabic language and script, in vivo. Design/methodology/approach Using quantitative and qualitative situational linguistic analyses of Classical Arabic, found in Quranic and religious texts scripts; Modern Standard Arabic, which is commonly used in formal Arabic channels; and dialectical Arabic, which varies hugely from one Arabic country to another: this study analyses rich marketing and consumer messages (tweets) – as a basis for developing an Arabic language social media methodological tool. Findings Despite the popularity of Arabic language communication on social media platforms across geographies, currently, comprehensive language processing toolkits for analysing Arabic social media conversations have limitations and require further development. Furthermore, due to its unique morphology, developing text understanding capabilities specific to the Arabic language poses challenges. Practical implications This study demonstrates the application and effectiveness of the proposed methodology on a random sample of Twitter data from Arabic-speaking regions. Furthermore, as Arabic is the language of Islam, the study is of particular importance to Islamic and Muslim geographies, markets and marketing. Social implications The findings suggest that the proposed methodology has a wider potential beyond the data set and health-care sector analysed, and therefore, can be applied to further markets, social media platforms and consumer segments. Originality/value To remedy these gaps, this study presents a new methodology and analytical approach to investigating Arabic language social media conversations, which brings together a multidisciplinary knowledge of technology, data science and marketing communications.

show abstract

“…Behdenna et al [21] used Arabic sentiment analysis to identify an opinion expressed in Arabic tweets. They collected 500 tweets and manually tagged them.…”

Section: Related Workmentioning

confidence: 99%

A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia

Aljameel

Alabbad

Alzahrani

et al. 2020

IJERPH

View full text Add to dashboard Cite

In March 2020, the World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) as a pandemic, which affected all countries worldwide. During the outbreak, public sentiment analyses contributed valuable information toward making appropriate public health responses. This study aims to develop a model that predicts an individual’s awareness of the precautionary procedures in five main regions in Saudi Arabia. In this study, a dataset of Arabic COVID-19 related tweets was collected, which fell in the period of the curfew. The dataset was processed, based on several machine learning predictive models: Support Vector Machine (SVM), K-nearest neighbors (KNN), and Naïve Bayes (NB), along with the N-gram feature extraction technique. The results show that applying the SVM classifier along with bigram in Term Frequency–Inverse Document Frequency (TF-IDF) outperformed other models with an accuracy of 85%. The results of awareness prediction showed that the south region observed the highest level of awareness towards COVID-19 containment measures, whereas the middle region was the least. The proposed model can support the medical sectors and decision-makers to decide the appropriate procedures for each region based on their attitudes towards the pandemic.

show abstract

Sentiment Analysis of Arabic Tweets: Opinion Target Extraction

Cited by 4 publications

References 10 publications

An Ontology-Based Approach to Enhance Explicit Aspect Extraction in Standard Arabic Reviews

An Ontology-Based Approach to Enhance Explicit Aspect Extraction in Standard Arabic Reviews

Mining Arabic Twitter conversations on health care: a new approach to analysing Arabic language on social media

A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia

Contact Info

Product

Resources

About