Sentiment Analysis of Arabic Algerian Dialect Using a Supervised Method

Abdelli, Adel; Guerrouf, Fayçal; Tibermacine, Okba; Abdelli, Belkacem

doi:10.1109/isacs48493.2019.9068897

Cited by 18 publications

(11 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We evaluated our model on the sentiment analysis task. For this reason, we used the Arabic reviews (Arev) dataset [23]. Using the Facebook API, the ARev dataset is built by more than 100 K comments of the most popular Algerian Facebook pages.…”

Section: Arev Datasetmentioning

confidence: 99%

AraBERT transformer model for Arabic comments and reviews analysis

Moubtahij

Abdelali

Tazi

2022

IJ-AI

View full text Add to dashboard Cite

Arabic language is rich and complex in terms of word morphology compared to other Latin languages. Recently, natural language processing (NLP) field emerges with many researches targeting Arabic language understanding (ALU). In this context, this work presents our developed approach based on the Arabic bidirectional encoder representations from transformers (AraBERT) model where the main required steps are presented in detail. We started by the input text pre-processing, which is, then, segmented using the Farasa segmentation technique. In the next step, the AraBERT model is implemented with the pertinent parameters. The performance of our approach has been evaluated using the ARev dataset which contains more than 40,000 comments-remarks records relate to the tourism sector such as hotel reviews, restaurant reviews and others. Moreover, the obtained results are deeply compared with other relevant states of the art methods, and it shows the competitiveness of our approach that gives important results that can serve as a guide for further improvements in this field.

show abstract

Section: Arev Datasetmentioning

confidence: 99%

AraBERT transformer model for Arabic comments and reviews analysis

Moubtahij

Abdelali

Tazi

2022

IJ-AI

View full text Add to dashboard Cite

show abstract

“…The model is evaluated for the Arabic Text Sentiment Analysis task, and it is based on the ARev (Arabic Reviews) dataset [15]. ARev is built from four sources.…”

Section: Data Collectionmentioning

confidence: 99%

Untitled

2022

INDJCSE

View full text Add to dashboard Cite

Sentiment analysis is a field of research that consists in analyzing the sensations, attitudes, and emotions of individuals towards entities such as products, services, and economic organizations. Likewise, the demand for Arabic Sentiment Analysis has grown rapidly due to the extensive use of the Arabic language in social media networks and has generated considerable interest from the research community. Arabic is one of the widely used languages on social networks. However, its morphological complexities, its dialect varieties, and its relatively few resources make it a challenging language for sentiment analysis. The main goal of our study is to implement and compare the performance of ASA by exploiting machine learning and deep learning models to automatically determine the sentiments by classifying them as positive or negative. For that, this study implements and evaluates a deep learning model namely the long short-term memory (LSTM) model, and three machine learning algorithms: Support vector machines (SVM), Logistic Regression (LR), K-Nearest neighbours (KNN). These classifiers are applied on the Arabic-Review (ARev) database that is manually annotated and collected from many Arabic resources. The results show that SVM and LR models are the best performing classifiers with an accuracy of 92% and 93% respectively.

show abstract

“…Recently, several efforts have been made to cover more DA variants. The authors of [2] have published an openaccess data set which serves to analyze sentiments for Arabic Algerian dialects. The data was mainly 10K comments collected from Facebook pages and manually annotated.…”

Section: Related Workmentioning

confidence: 99%

An open access NLP dataset for Arabic dialects : Data collection, labeling, and model construction

Boujou,

Chataoui,

Mekki

et al. 2021

Preprint

View full text Add to dashboard Cite

Natural Language Processing (NLP) is today a very active field of research and innovation. Many applications need however big sets of data for supervised learning, suitably labelled for the training purpose. This includes applications for the Arabic language and its national dialects. However, such open access labeled data sets in Arabic and its dialects are lacking in the Data Science ecosystem and this lack can be a burden to innovation and research in this field. In this work, we present an open data set of social data content in several Arabic dialects. This data was collected from the Twitter social network and consists on +50K twits in five (5) national dialects. Furthermore, this data was labeled for several applications, namely dialect detection, topic detection and sentiment analysis. We publish this data as an open access data to encourage innovation and encourage other works in the field of NLP for Arabic dialects and social media. A selection of models were built using this data set and are presented in this paper along with their performances.

show abstract

Sentiment Analysis of Arabic Algerian Dialect Using a Supervised Method

Cited by 18 publications

References 9 publications

AraBERT transformer model for Arabic comments and reviews analysis

AraBERT transformer model for Arabic comments and reviews analysis

Untitled

An open access NLP dataset for Arabic dialects : Data collection, labeling, and model construction

Contact Info

Product

Resources

About