AraSenTi-Tweet: A Corpus for Arabic Sentiment Analysis of Saudi Tweets

Al-Twairesh, Nora; Al‐Khalifa, Hend S.; Al-Salman, AbdulMalik S.; Al-Ohali, Yousef

doi:10.1016/j.procs.2017.10.094

Cited by 114 publications

(73 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [10], the authors presented a model for sentiment analysis of Saudi Arabic tweets to extract feedback from Mubasher products. In [11], the authors developed Corpus for Arabic Sentiment Analysis of Saudi Tweets. In [12], the authors explained how mining social networks can be done on Arabic Slang comments by proposing a SVM based classifier that applies sentiment analysis to classify youth news comments on Facebook.…”

Section: Related Workmentioning

confidence: 99%

“…It is done by removing any attached suffixes, prefixes, and/or infixes from words in tweets. A stemmed word represents a broader concept to the original word, also it may lead to save storage [11]. The goal of stemming tweets is to reduce the derived or inflected words into their stems, base or root form in order to improve SA.…”

Section: )mentioning

confidence: 99%

“…Many researchers used supervised learning approaches on data related to publically released corpuses for Arabic SA [11]. Such researches use the Modern Standard Arabic (MSA).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sentiment Analysis of Arabic Jordanian Dialect Tweets

Atoum¹,

Nouman²

2019

ijacsa

View full text Add to dashboard Cite

Sentiment Analysis (SA) of social media contents has become one of the growing areas of research in data mining. SA provides the ability of text mining the public opinions of a subjective manner in real time. This paper proposes a SA model of Arabic Jordanian dialect tweets. Tweets are annotated on three different classes; positive, negative, and neutral. Support Vector Machines (SVM) and Naïve Bayes (NB) are used as supervised machine learning classification tools. Preprocessing of such tweets for SA is done via; cleaning noisy tweets, normalization, tokenization, namely, Entity Recognition, removing stop words, and stemming. The results of the experiments conducted on this model showed encouraging outcomes when Arabic light stemmer/segment is applied on Arabic Jordanian dialect tweets. Also, the results showed that SVM has better performance than NB on such tweets' classifications.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: )mentioning

confidence: 99%

See 1 more Smart Citation

Sentiment Analysis of Arabic Jordanian Dialect Tweets

Atoum¹,

Nouman²

2019

ijacsa

View full text Add to dashboard Cite

show abstract

“…In the same context and sharing the same objective, that of enriching the resources available for sentiment analysis applications in dialectal Arabic, other works have presented datasets in different dialects, including, but not limited to, the following: In Saudi dialect, the Arasenti-tweet [27], a dataset retrieved on Twitter and manually annotated in four classes (positive, negative, neutral and mixed), other datasets have been reported in [28] [29]. With regard to the Jordanian dialect, different datasets gathered on Facebook as well as Twitter, were introduced in [30][31] [32].…”

Section: B Vernacular Arabic Datasetsmentioning

confidence: 99%

MSTD: Moroccan Sentiment Twitter Dataset

Mihi¹,

Ait²,

El³

et al. 2020

IJACSA

View full text Add to dashboard Cite

With the proliferation of social media and Internet accessibility, a massive amount of data has been produced. In most cases, the textual data available through the web comes mainly from people expressing their views in informal words. The Arabic language is one of the hardest Semitic languages to deal with because of its complex morphology. In this paper, a new contribution to the Arabic resources is presented as a large Moroccan dataset retrieved from Twitter and carefully annotated by native speakers. For the best of our knowledge, this dataset is the largest Moroccan dataset for sentiment analysis. It is distinguished by its size, its quality given by the commitment of annotators, and its accessibility for the research community. Furthermore, the MSTD (Moroccan Sentiment Twitter Dataset) is benchmarked through experiments carried out for 4-way classification as well as polarity classification (positive, negative). Various machine-learning algorithms are combined to feature extraction techniques to reach optimal settings. This work also presents the effect of stemming and lemmatization on the improvement of the obtained accuracies.

show abstract

“…The second module refers to trustworthy Arab news websites and relies on cosine similarity to determine to which extent is the tweet content alike to the trustworthy reference. The third module relies on Arabic sentiment library AraSenTi (Al-Twairesh et al, 2017). It also relies on a set of Arabic and Saudi variant of Arabic words that indicate falsifying or supporting information.…”

Section: Related Workmentioning

confidence: 99%

Twitter Users Crediblity Evaluation Based on Social Graph Impression

Slimi¹,

Bounhas²,

Slimani³

2019

Proceedings of the 16th International Conference on Applied Computing 2019

View full text Add to dashboard Cite

In the era of social media, information became abundant, versatile and easily accessible. Such characteristics enable almost anyone to publish and share their opinion about a variety of subjects. Such freedom comes at a cost since information and source credibility are in jeopardy. Therefore, numerous solutions were proposed to evaluate source credibility. In this paper, we review previous efforts on twitter credibility evaluation. We also propose an approach that relies on the impression of user social network on his tweets to determine his credibility. Furthermore, we incorporate a feature that determines the credibility of the URLs shared by the user through his tweets. The evaluation of our approach has shown it outperforms the baseline in both precision and recall.

show abstract

AraSenTi-Tweet: A Corpus for Arabic Sentiment Analysis of Saudi Tweets

Cited by 114 publications

References 23 publications

Sentiment Analysis of Arabic Jordanian Dialect Tweets

Sentiment Analysis of Arabic Jordanian Dialect Tweets

MSTD: Moroccan Sentiment Twitter Dataset

Twitter Users Crediblity Evaluation Based on Social Graph Impression

Contact Info

Product

Resources

About