Faculty and staff publications

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Browsing Faculty and staff publications by Author "A. Al Shamsi, Arwa"

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    Ensemble Stacking Model for Sentiment Analysis of Emirati and Arabic Dialects
    (ScienceDirect, 2023) A. Al Shamsi, Arwa; Abdallah, Sherief
    Sentiment analysis is the process of examining people’s opinions and emotions towards goods, services, organizations, individuals, and other things, through the use of textual data. It involves categorizing text as positive, negative, or neutral to quantify people’s beliefs. Social media platforms have become an important source of sentiment analysis data due to their widespread use for sharing opinions and infor mation. As the number of social media users continues to grow, the amount of data generated for senti ment analysis also increases. Previous research on sentiment analysis for the Arabic language has mostly focused on Modern Standard Arabic and various dialects such as Egyptian, Saudi, Algerian, Jordanian, Tunisian, and Levantine. However, there has been no research on the utilization of deep-learning approaches for sentiment analysis of the Emirati dialect, which is an informal form of the Arabic language spoken in the United Arab Emirates. It’s important to note that each country in the Arab world has its dialect, and some dialects may even have several sub-dialects. The primary aim of this research is to create a highly advanced deep-learning model that can effectively perform sentiment analysis on the Emirati dialect. To achieve this objective, the authors have proposed and utilized seven different deep-learning models for sentiment analysis of the Emirati dialect. Then, an ensemble stacking model was introduced to combine the best-performing deep learning models used in this study. The ensemble stacking deep learning model consisted of deep learning models with a meta learner layer of classifiers. The first model combined the two best-performing deep learning models, the second combined the four best-performing models, and the final model combined all seven trained deep learning models in this research. The proposed ensemble stacking deep learning model was assessed on four datasets, three versions of the ESAAD Emirati Sentiment Analysis Annotated Dataset, two versions of Twitter-based Benchmark Arabic Sentiment Analysis Dataset (ASAD), an Arabic Company Reviews dataset, and an English dataset known as a Preprocessed Sentiment Analysis Dataset PSAD. The results of the experiments demonstrated that the proposed ensemble stacking model presented an outstanding performance in terms of accuracy and achieved an accuracy of 95.54% for the ESAAD dataset, 96.71% for the ASAD benchmark dataset, 96.65% for the Arabic Company Reviews Dataset, and 98.53% for the Preprocessed Sentiment Analysis Dataset PSAD.
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    Sentiment Analysis of Emirati Dialect
    (MDPI, 2022) A. Al Shamsi, Arwa; Abdallah, Sherief
    : Recently, extensive studies and research in the Arabic Natural Language Processing (ANLP) field have been conducted for text classification and sentiment analysis. Moreover, the number of studies that target Arabic dialects has also increased. In this research paper, we constructed the first manually annotated dataset of the Emirati dialect for the Instagram platform. The constructed dataset consisted of more than 70,000 comments, mostly written in the Emirati dialect. We annotated the comments in the dataset based on text polarity, dividing them into positive, negative, and neutral categories, and the number of annotated comments was 70,000. Moreover, the dataset was also annotated for the dialect type, categorized into the Emirati dialect, Arabic dialects, and MSA. Preprocessing and TF-IDF features extraction approaches were applied to the constructed Emirati dataset to prepare the dataset for the sentiment analysis experiment and improve its classification performance. The sentiment analysis experiment was carried out on both balanced and unbalanced datasets using several machine learning classifiers. The evaluation metrics of the sentiment analysis experiments were accuracy, recall, precision, and f-measure. The results reported that the best accuracy result was 80.80%, and it was achieved when the ensemble model was applied for the sentiment classification of the unbalanced dataset.
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    Text Mining Techniques for Sentiment Analysis of Arabic Dialects: Literature Review
    (ASTESJ, 2021) A. Al Shamsi, Arwa; Abdallah, Sherief
    Social media attracts a lot of users around the world. Many reasons drive people to use social media sites such as expressing opinions and ideas, displaying their diaries and sharing them with others, social communication with family and friends and building new social relationships, learning and sharing knowledge. Written text is one of the most common forms used for communication while using social media sites. People use written texts in different languages, and due to the increased usage of social networking sites around the world, the amount of texts and data resulting from this use is large. These generated data considered as a valuable source of information that attracted business owners, companies, government institutions, and of course, it attracts researchers and data scientists as well. Researchers and data scientists increasingly presented great efforts in investigating and analyzing Arabic Language texts. Most of these efforts targeted the Modern Standard form of Arabic Language. While exploring the social media sites, most of the Arab users tend to use their dialects while utilizing Social Media sites, which results in generating a massive amount of Arabic Dialects texts. The number of researches and analysis of Dialects' form of the Arabic language are limited, however, it is increasing recently. This literature review aims to explore approaches and methods used for Sentiment Analysis of Arabic Dialects text.