کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6902051 | 1446498 | 2017 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Challenges in Sentiment Analysis for Arabic Social Networks
ترجمه فارسی عنوان
چالش ها در تجزیه و تحلیل احساسات برای شبکه های اجتماعی عربی
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کلمات کلیدی
رسانه های اجتماعی عربی، توییتر، گویش عربی، پردازش زبان طبیعی، نظر معادن، تجزیه و تحلیل احساسات، فراگیری ماشین،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
چکیده انگلیسی
There has been an enormous increase in the volume of Arabic posts on many social networks, providing a rich source of opinions on a variety of topics. Opinion mining has attracted much research interest in the last decade, but has mostly been conducted using the English language. Opinion mining from Arabic social media is lagging behind, primarily due to challenges with processing the morphologically complex Arabic natural language and the lack of available tools and resources for extracting Arabic sentiments from text. This complex task is further exacerbated when dealing with Saudi dialects that do not abide by the formal grammatical structure of Modern Standard Arabic (MSA). Targeting the use-case of unemployment in Saudi Arabia, this paper investigates the major challenges facing sentiment analysis researchers of the informal Arabic language and documents how these challenges can be initially addressed by means of linguistic pre-processing of raw text and supervised machine learning (ML) to identify sentiment polarity. The initial experimental evaluation yielded satisfactory sentiment classification results while also demonstrating the benefit of using ML to identify the dominant features in deciding sentiment polarity, which can in turn be used in constructing a problem-domain knowledge base that can further improve the accuracy of sentiment classification.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Procedia Computer Science - Volume 117, 2017, Pages 89-100
Journal: Procedia Computer Science - Volume 117, 2017, Pages 89-100
نویسندگان
Ghadah Alwakid, Taha Osman, Thomas Hughes-Roberts,