Rabail Zahid
ABSTRACT
Social 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.
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Index Terms
- Roman Urdu reviews dataset for aspect based opinion mining
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