Aspects Detection Model for Users’ Reviews Using Machine Learning Techniques | ||
| النشرة المعلوماتية في الحاسبات والمعلومات | ||
| Volume 5, Issue 1 - Serial Number 20230501, January 2023, Pages 1-6 PDF (1.12 M) | ||
| Document Type: المقالة الأصلية | ||
| DOI: 10.21608/fcihib.2022.85188.1052 | ||
| Authors | ||
| سعد خليفه* ; محمد مرعي; مي الدفراوي | ||
| نظم المعلومات , الحاسبات و الذکاء الاصطناعي , حلوان , القاهره ,مصر | ||
| Abstract | ||
| sentiment analysis (SA) is an important field that is concerned with capturing user emotions, modes, and interests through a written text. sentiment analysis (SA) and opinion mining has become a trendy topic today it helps us to catch the emotions of the speaker whether sad or happy etc. As well as some governments may use this field to catch the emotions of their people whether they are satisfied with the way they use to control the country or not. The number of people using social media is rapidly increasing because many people use it to share their opinions, feeling, and emotions so social media platforms like Twitter or Facebook possess a large volume of data for example lots of tweets are posted on Twitter every day, that can be analyzed to capturing the current state or mode of the author. Currently, in the age of coronavirus, many companies obliged their employees to work from home millions of tweets are posted people use this platform to share their opinion, feeling, and their new lifestyle during quarantine. The volume of data become larger, and it became a fertile material for analysis to know how people feel and how they adapt to the new situation. The research objective is to provide a review of using aspect-based sentiment analysis to detect user satisfaction and apply this technique to such a trendy topic, which is working from home during a covid-19 pandemic. In addition to detecting problems, they face. | ||
| Keywords | ||
| Sentiment Analysis (SA); Opinion Mining; Social Media; Aspect-Based Sentiment Analysis; Machine Learning (ML) | ||
| References | ||
|
| ||
|
Statistics Article View: 375 PDF Download: 154 |
||
