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MULDASA: Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media
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In: Applied Sciences; Volume 12; Issue 8; Pages: 3806 (2022)
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Knowledge and Attitudes of Medical Students toward COVID-19 Vaccine in Saudi Arabia
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In: Vaccines; Volume 10; Issue 4; Pages: 541 (2022)
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Glottolog 4.4 Resources for Saudi Arabian Sign Language
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: Max Planck Institute for Evolutionary Anthropology, 2021
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Knowledge and Perception of Registered Nurses Regarding the Scope of Practice of Speech-Language Pathologists
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In: International Journal of Environmental Research and Public Health ; Volume 18 ; Issue 19 (2021)
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A Comparative Study of Arabic Part of Speech Taggers Using Literary Text Samples from Saudi Novels
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In: Information; Volume 12; Issue 12; Pages: 523 (2021)
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Social and personal engagement and development during study abroad: experiential perspectives from Saudi language learners
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The association of second language learning motivation and target-like article production in English-major female Saudi students: a sociolinguistic approach
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Saudi Mothers' Experiences Maintaining Their Young Children's Arabic Language and Islamic-Saudi Identity
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A taxonomy of antonymy in Arabic: Egyptian and Saudi proverbs in comparison
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In: Open Linguistics, Vol 7, Iss 1, Pp 200-222 (2021) (2021)
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The Self-perceptions of NNESTs in Saudi Arabian Public Schools Regarding Their Teaching Abilities and Their Views About English Language-in-education Policy
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Examining a French Program In Saudi Arabia for Evidence of CLIL Practices ...
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Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning
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In: Applied Sciences ; Volume 10 ; Issue 4 (2020)
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Analysis of the relationship between Saudi twitter posts and the Saudi stock market
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Identifying Mubasher software products through sentiment analysis of Arabic tweets
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Abstract:
Social media has recently become a rich resource in mining user sentiments. In this paper, Twitter has been chosen as a platform for opinion mining in trading strategy with Mubasher products, which is a leading stock analysis software provider in the Gulf region. This experiment proposes a model for sentiment analysis of Saudi Arabic (standard and Arabian Gulf dialect) tweets to extract feedback from Mubasher products. A hybrid of natural language processing and machine learning approaches on building models are used to classify tweets according to their sentiment polarity into one of the classes positive, negative and neutral. Firstly, document's Pre-processing are explored on the dataset. Secondly, Naive Bayes and Support Vector Machines (SVMs) are applied with different feature selection schemes like TF-IDF (Term Frequency-Inverse Document Frequency) and BTO (Binary-Term Occurrence). Thirdly, the proposed model for sentiment analysis is expanded to obtain the results for N-Grams term of tokens. Finally, human has labelled the data and this may involve some mistakes in the labelling process. At this moment, neutral class with generalisation of our classification will take results to different classification accuracy.
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Keyword:
Arabic; data mining; Mubasher; pre-processing; Saudi Arabia; sentiment analysis; text mining; Twitter
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URL: https://doi.org/10.1109/ICCSII.2016.7462396 http://hdl.handle.net/10547/623853
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Integrating computer-assisted language learning in Saudi schools: a change model
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An investigation of the use of compliments by Saudi Arabian students
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