Department of Computer Science
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Item Structural based Sentiment Mining for Roman Urdu(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Mubashir Ubaid Ullah; SP18-RCS-013; LHR TP 5986; Dr. Muhammad Waqas AnwarWeb-based data is increasing day by day and plays a vital role in developing people’s opinions. Sentiment mining/analysis is the natural language processing task that helps to identify, classify these opinions. Usually research focus is on resource-rich language for sentiment mining. In this thesis, we performed classification of various sentiments using feature selection techniques for a resource-poor language i.e. Roman Urdu. These classification techniques include chi-square, mutual information and select from model which are implemented on the Roman Urdu Dataset of 11k reviews. Well-known machine learning algorithms are applied for experimental analysis that includes Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Multinomial Naïve Bayes (MNB) and Multi-Layer Perceptron (MLP). These are applied for character-level & word-level features for n-gram variations that are bigram to 7-gram for character-level classification and Uni, Bi, Uni Bi gram, Uni-Bi-Tri gram & Uni-Bi-Tri-Four gram in terms of word-level classification. Results are being evaluated using accuracy, precision, recall & f1-score. The Highest accuracies for both word-level and character-level achieved are 83.93% and 83.72% which improves the baseline score that was 82.46% on feature union whereas F1-score is 90.51% & 90.42% respectively. Some renowned Neural Network techniques are also applied in this thesis which include CNN, LSTM, & Bi-LSTTM. We achieved maximum results by Bi-LSTM which gives 91.8% accuracy and 91.7% F1-scoreItem Urdu to English Based Unsupervised Machine Translation(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Ahmed Raza; SP18-RCS-005; LHR TP 5983; Dr. Muhammad Waqas AnwarThe process of automatically converting the text from one language to another natural language is Machine Translation. Machine Translation is a subfield of computational linguistic. There are two state-of-the-art machine translation techniques i,e Neural Machine Translation (NMT), and Statistical Machine Translation (SMT). In both techniques, a large corpus is required for the training of the translation model. Urdu counts in low resource languages due to the fewer resources available for computational work. To build a good translation system available resources are not enough. Many languages present in the world have a different structure. Like in Urdu and English, Urdu structure is based on Subject Object-Verb (SOV) and the English structure is based Subject-Verb-Object (SVO). In this study, we presented Urdu to English unsupervised translation model and the practical challenges faced during the work. We try to partially remove the need for parallel corpora and proposed a method to train a Machine Translation System in an unsupervised manner. The proposed system is aimed to provide Urdu to English translation through an unsupervised manner. For this propose, we use Artetxe Author developed a toolkit that is based on Unsupervised Neural Machine Translation (UNMT). This approach tested the models of UNMT which include denoising and on-the-fly back-translation. From denoising model obtain the BLEU score 4.14 and 5.11 for two language pairs UR-EN and EN-UR. From back translation obtain the BLEU score of 5.21 and 6.28 which are better than from the previous score. Back-translation results difference from denoising technique gains +1.07 and +1.17 for two language pairs Urdu to English and English to Urdu. We also faced many challenges during work and effects on pre-processing techniques. Our approach shows promising results in translation of Urdu text into English which is mostly neglected due to its complexities