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تحليل الآراء في تويتر
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محمّد مصطفى حجّوز( باحث )
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قمنا بإحضار البيانات من صفحات مواقع التواصل الاجتماعي تويتر، ثم عملنا عليها عملية تنظيف و تجهيز للنص من أجل عملية التصنيف فالنصوص المسترجعة تحتوي على الكثير من الضجيج و المعلومات غير المفيدة المتعلقة بعملية تحليل الآراء مثل الاعلانات و الروابط و عناوين البريد الالكتروني و وجود العديد من الكلمات التي لا تؤثر على التوجه العام للنص، و بعد الحصول على كل المنشورات في صفحة الفيسبوك و ما هي التعليقات الخاصة حول كل المنشور المراد معرفة النسبة المئوية للآراء الإيجابية و الآراء السلبية له. طبّقنا خوارزمية بايز في التصنيف و أجرينا عليها التدريب المناسب و بعد تمرير بيانات التغريدات (الآراء) حصلنا على نتائج جيدة حول نسبة المؤيدين للمنشور و نسبة المعارضين له.
We bring the data from the social networking site Twitter pages, and then we have worked on cleaning and processing operation to the text of for the classification process texts retrieved contain a lot of noise and information is useful for the process of analyzing the views, such as advertisements and links and e-mail addresses and the presence of many words that do not affect the general orientation of the text, and then get all the publications in the Twitter page and what are the comments about each tweets is intended to know the proportion of supporters and opponents of this publication. We apply Naïve Bayes algorithm in classification, we had the appropriate training, and after passing Posts and comments data (opinions), we got good results on the ratio of supporters of the post and the percentage of his opponents.
References used
Data Mining Concepts and Techniques Second Edition Jiawei Han and MichelineKamber
H. Tang, S. Tan, X. Cheng, A survey on sentiment detection of reviews, Expert Systems with Applications 36 (7) (2009) 10760 10773
Wilson T, Wiebe J, Hoffman P. Recognizing contextual polarity in phrase-level sentiment analysis
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