A New Opinion Review Methodology for Arabic Twitter Sentiment Analysis
نام عام مواد
[Thesis]
نام نخستين پديدآور
Almalki, Abdullah S.
نام ساير پديدآوران
Taylor, Bradley
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
The Catholic University of America
تاریخ نشرو بخش و غیره
2020
يادداشت کلی
متن يادداشت
63 p.
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
M.S.C.S.
کسي که مدرک را اعطا کرده
The Catholic University of America
امتياز متن
2020
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
Each day, a tremendous number of opinions are posted about different issues and different subjects from massive active users on social media. While people increasingly use social media applications such as Twitter to express their feelings and opinions about many things such as events, movies, services and products; this data may also be used as a more valuable resource for governments, companies and decision-makers informing their decisions. This research proposed to use a machine learning approach and Lexicon approach to extract opinions of groups of people from Arabic tweets. In this research I used an entertainment event, as a case study. This research focuses on assessing peoples' opinions more accurately by leveraging the Retweet and Favorite, providing a similar effect as the tweet itself, as well as removing tweets unrelated to the subject opinion, such as: news, advertisements and spam. This research redounds to extend current knowledge of social media analysis and machine learning in the field of opinion analysis. This provides a methodology for selecting effective sentiment analysis mechanisms for Arabic tweets; in particular, accurate results using the developed machine learning algorithm.
اصطلاحهای موضوعی کنترل نشده
اصطلاح موضوعی
Artificial intelligence
اصطلاح موضوعی
Computer science
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )