Paper Info
Title
Roman Urdu reviews dataset for aspect based opinion mining
Abstract
ABSTRACTSocial media, today, demonstrates the rapid growth of modern society as it becomes the main platform for Internet users to communicate and express themselves. People around the world, use a number of devices and resources to access the Internet, set up social networks, conduct online business, e-commerce, e-surveys, etc. Currently, social media is not only a technology that provides information to consumers, it also encourages users to connect and share their views and perspectives. It leads to an increase in inspiration towards Opinion Mining (OM), which is important for both customers and companies in making decisions. Individuals like to see the opinions provided by other customers about a particular product or a service. Companies need to analyze their customer's feedback to strengthen their business decisions. A lot of research has been performed in various languages in the field of Aspect Based OM (ABOM). However, there are still certain languages that need to be explored, such as Roman Urdu (RU). This paper presents a proposed reviews data-set (a RU data-set) of mobile reviews that has been manually annotated with multi-aspect sentiment labels at the sentence-level. It presents base-line results using different Machine Learning (ML) algorithms. The results demonstrate 71% F1-score for aspect detection and 64% for aspect-based polarity.
Year
DOI
Venue
2020
10.1145/3417113.3423377
ASE
Keywords
DocType
ISSN
RU Reviews, Data pre-processing, OM, Baseline approaches
Conference
2151-0830
ISBN
Citations 
PageRank 
978-1-7281-7295-8
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Rabail Zahid100.34
Muhammad Owais Idrees200.34
Hasan Mujtaba3225.32
Mirza Omer Beg400.68