SENTIMENT ANALYSIS OF ROMAN URDU REVIEWS OF PSL ANTHEMS
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Date
2021
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UMT, Lahore
Abstract
Easy access and economic availability of Computers, Tabs, Smartphones and high—speed internet people are now using the web for Social interaction and Business correspondence. People are becoming habitual to posting their reviews about any specific entity/product, they used. These reviews are very helpful for both—users and sellers. Initially, these reviews are not too much they can easily be analyzed by reading them. The continuous increase in the amount of these reviews creates a need that reviews can be analyzed and useful patterns to be found and explored through the automated channels. This need leads to a new field in the domain of research known as “Sentiment Analysis”. Sentiment Analysis is the study of people’s opinions, sentiments, attitudes and emotions expressed in written language also said that it is a process of categorizing people’s opinions expressed in the piece of text, especially to determine whether the writer’s attitude towards a particular topic or product is positive, negative, or neutral. The PSL anthems are released every year before the start of the league. There is no work is witnessed on PSL Sentiment Analysis to know the behaviors of the listeners towards the PSL anthems. This research is targeting the sentiment analysis of these reviews of PSL anthems and proposed a model to analyze Roman Urdu Reviews. In this thesis, five different Machine Learning algorithms are used for text classification of reviews by using Rapid Miner Tool. The thesis presents a Sentiment Analysis of Roman Urdu reviews on PSL Anthems available on YouTube. These reviews are scraped, pre-processed and analyzed using Naïve Bayes, Gradient Boost Tree, Support Vector Machine, K-Nearest Neighbors and Artificial Neural Network. The Roman Urdu Sentiment Analysis is performed at 7000 bi-lingual manual annotated reviews. The Naïve Bayes and Logistic Regression correctly predicted 68.86% of reviews. ANN achieved 68.86% on the testing dataset and 69.71% on the validation of the results.