Computational Linguistic Approach for Holistic User Behaviors Modeling Through Opinionated Data of Virtual Communities

Abstract

This research is aimed at establishing a computational linguistic model for the detection of positive and negative statements, synthesized for the Pakistani microblogging site Twitter, particularly, in the Roman Urdu language. With increased freedom of speech people express their sentiments towards an event or a person in positive, negative, neutral, and sometimes sarcastic tones, especially on social media platforms. Pakistani social media users, like other multilingual countries, express their opinions through code switching and code mixing. Their language lacks correct grammar, informal and nonstandard writing, unrelated spelling, alternative analogies make it difficult for computational linguist to mine their data for computational research. To overcome this challenge, the study employed web scraping tools to retrieve a large number of Roman Urdu tweets. In order to establish a new positive and negative statements corpus, the text data is annotated through a sentiment analysis carried out by using TextBlob sentiment analysis and Bidirectional Encoder Representations from Transformers (BERT). Addressing this issue makes it possible to eliminate the gap that is evident in the models that do not identify Roman Urdu as a form of language. The findings are useful for the regulatory bodies and researchers since it offers a culturally and linguistically appropriate database and model targeting resource constraints and key performance metrics. It helps in content moderation and in making policies regarding the technological advancement within Pakistan.