Anti-Social Behavior Detection in Urdu Language Posts of Social Media
Muhammad Abdullah Sohail, Azhar Imran, Hameed Ur Rehman, Muhammad Salman · 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) · 2020
Social media such as Facebook and Twitter has gained strong attention for sharing information, worldwide connectivity and brand marketing all over the globe. The prevalent use of social networking sites has produced exceptional amounts of data. The mining of these social media applications has its potential to excerpt illegal activities which may be helpful for individuals, business and customers. The mining of data obtained from Facebook and Twitter, can be used to predict emotions of stakeholder and its analysis can provide very valuable information regarding behavioral inclinations of the writer. The automatic sentiment analysis to detect the emotional content in textual data has been widely used in many research fields. Most of the existing sentiment analysis techniques are tailored for English Language. This paper presents Urdu based antisocial behavior detection (ASB). We aim in particular to establish antisocial behavior detection method and defining its emotional state. We are intended to introduce a sentiment analysis based behavioral model that describes emotions related to antisocial behavior. In addition to describing the negative emotional state, our model will also use the concept of behavioral tendencies and evidence to predict the possible behavior of social media activists based on input text. We will outline the design of behavior detection systems based on social media posts. The learning algorithm learn about emotions from social media posts in the Roman Urdu language to predict user's behavior regarding any specific post. The results of this study has verified that our method has outperformed state-of-the-art methods in terms of accuracy. A bilingual or multilingual ASB approach can be made in future.