M.Phil / MS
Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/36
This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.
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Item Aspect Based Sentiment Analysis for Urdu Language Using Rule-Based Approac(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) EHTESHAM HASHMI; FA20-RCS-009; Dr. Muhammad Waqas Anwar; LHR TP 8241Aspect Based Sentiment Analysis (ABSA) also known as entity level analysis has gained the vital importance to extract the people’s sentiment, emotions or opinions towards some entity. This entity can be any organization, social platforms etc. In spite of the fact that Sentiment Analysis (SA) has been examined broadly within the English language domain with some eminent work in other dialects including Chinese, Arabic etc., many other resource poor languages including Urdu did not get much attention of researchers due to the lack of resources. Urdu is widely spoken language in all over the world and various social platforms are full of Urdu reviews containing people’s sentiments. In this study we have established an aspect level rule-based approach for the Urdu language. To perform our work, we have used the Urdu dialect dataset containing the “COVID-19” tweets. These tweets contain all the information related to Coronavirus and people’s views towards this disease. Sentiment lexicon has been used to extract for the opinion term present in a tweet and after getting the opinion term various aspect have been extracted associated with the opinion. Aspect extraction has been performed using various rule, these rules have been created using linguistic and syntactic context of the phrases present in the tweet and polarities are assigned accordingly. The proposed study focused to achieve four crucial modules: aspect term, aspect term polarity, aspect term category and aspect term category polarity. With the help of evaluation measures including F1-Score, Accuracy, Precision and Recall our work has achieved the promising results.Item A Comparative Analysis of Offensive Language Detection in Roman Urdu(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Muneeb Rehman Khan; FA19-RCS-019; LHR TP 8305; Dr. Muhammad Waqas AnwarThe rise of social media has encouraged experts to conduct several studies based on user generated data. The widespread use of online platforms like Facebook, Twitter and other web forums has created challenges to keep check for inappropriate language. Pakistan has about 44 million online social network users that speak Roman Urdu to interact with each other. The prevalence of user-generated information on social media caused a massive rise in the content of offensive texts. However, this growth also offers avenues for trolls who contaminate these social platforms with their nasty remarks. In South Asia, Roman Urdu is the most widely used communication language on social media. Compared to other resource-rich languages such as English, less work has been done on offensive language classification in Roman Urdu. In this research (1) a lexicon of offensive terms in Roman Urdu is presented, (2) developed a dataset called RU-OLD “Roman Urdu Offensive Language Detection” with 15000 tweets, and (3) conducted a comparative analysis of machine learning and several deep learning architectures. Comparative analysis was performed by well-known machine learning classifiers such as Naive Bayes, RF, SVM, and Logistic Regression as well as cutting-edge deep neural networks such as Long Short-Term Memory, Convolutional Neural Networks, XLNET and BERT. XLNET trained with developed corpus outperformed the rest of the models and resulted in the highest accuracy of 93%. The results are suggestive of the scalability of the proposed approach on large scale to counter offensive language in Roman UrduItem Cross Domain Sentiment Classification for Urdu Language(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Sana Hassan; FA18-RCS-010; LHR TP 6410; Dr. Muhammad Waqas AnwarAn enormous amount of information is produced daily on the internet about different prod ucts and objects. People like to express their feelings, thoughts in their native language on different social sites. This bulk data needs to be interpreted. So, Sentiment Analysis (SA) is required which extracts people’s opinions, feelings, and thoughts. However, it is a highly domain-dependent task. Due to this, the issue of domain-transfer arises. If a classifier is tested with any different domain, other than on which it is trained, its performance is affected. Many tasks and frameworks are created in mostly English and western languages. Tasks intended for the English language cannot be applied for other languages, hence there is a need to work on different dialects. In this research, we performed a cross-domain sentiment Analysis on data set of Urdu language comprising of 9000 sentences from sports tweets in which there are two domains (Hockey and Cricket). Furthermore, preparing corpus for specific domains in the Urdu language we applied ma chine learning and deep learning approach. After this, we evaluated results using standard evaluation measures and a confusion matrix, Gated Recurrent Units (GRU) gives the highest accuracy of (77%)Item Neural Architecture Based Urdu Named Entity Recognition(Library Information Services COMSATS University Islamaabad Lahore Campus, 2020) Muhammad Ehsan; FA18-RCS-018; LHR TP 6413; Dr. Muhammad Waqas AnwarIn natural language processing, named entity recognition (NER) is the issue of figuring out and arrange named elements referenced in unstructured content into pre-characterized classifications. To compare, evaluate and develop a NER system, we need standard and high-quality benchmark resources. But unfortunately, corpora in a huge amount are available for many other languages but not for the Urdu language. Even though Urdu is a rich language in all aspects and has more than 300 million speakers all over the world. As state-of-the-art sequence labeling systems are heavily based on large amounts of domain specific knowledge and hand-crafted feature for efficient learning from small data. When the dataset is in a large amount and high quality then the Named Entity Recognition (NER) task can be solved in a better way and provides better results on a large amount of labeled data, but the availability of labeled data in most of the languages is limited for training purpose. One way is there are scarcely any limitations on identifiable words. Hence, concluded the little amount of exampled data is troublesome. Subsequently, deliberately built orthographic features and language-explicit information assets (e.g., gazetteers) are broadly utilized for understanding this undertaking. Tragically, for new languages and new domains, the task of identifying explicit language sources and feature extraction is mainly an expensive process. As a result, it makes efficient NER system development adaptation a challenge. Another way to makes an efficient NER system is the use of deep neural network models that have achieved highest performance in the domain of natural language processing (NLP). However, this performance highly depends on the availability of huge amount of annotated data. The other way to get a better generalization from a small amount of data is to deploy semi-supervised models that performs on small set of annotated examples and a large set of unannotated data. Due to limited annotated data for many Natural Language Processing (NLP) problems like NER for the Urdu language, we will solve the NER problem using deep neural algorithms. In deep learning methods, we perform 44 experiments by using recurrent neural network (RNN), long short-term memory (LSTM), Bidirectional LSTM, gated recurrent unit (GRU), Bidirectional GRU and GRUCell in a supervised learning setting to compare with ix the previous models. Our model outperformed the previous results with F1-score of 91.43%. We also use multi-layer perceptron in a semi-supervised setting. This model gives the results with F1-score of 51.00%. We use standard evaluation measures to check the performance of the NER system. However, deep learning results with semi-supervised settings unable to beat the results of deep learning results with supervised settings.Item Resource Creation and Evaluation of Aspect Based Sentiment Analysis in Urdu(Library Information Services COMSATS University Islamaabad Lahore Campus, 2020) LHR TP 6412; Dr. Muhammad Waqas Anwar; FA18-RCS-017; Sadaf RaniSentiment analysis is an emerging research area of Natural Language Processing (NLP). Along with the rise of people generated content on social sites, sentiment analysis has gained more importance. One of the challenging tasks of sentiment analysis is Aspect Based Sentiment Analysis (ABSA). ABSA is a task of identifying the sentiment at aspect level. Right now, mostly researchers have focused on the English language but very less attention on resource poor languages like Urdu, Arabic etc. To the best of our knowledge, there is no publicly available dataset on ABSA in Urdu language. In this study, we focused on the resource creation for the task of ABSA in Urdu language. This task is further divided into four subtasks i.e., Aspect Term, Aspect Term Polarity, Aspect Category and Aspect Category Polarity. Our dataset is comprised of 6672 sentences which are collected form twitter using Twitter API’s. For annotation, we prepared standard guidelines which are according to the SemEval. Evaluation is also performed by extracting n gram features at word level and char level. TF-IDF vectorizer is used to vectorize the data into machine readable form. Then we applied different machine learning algorithms. Experiments are performed by Naïve Bayes (NB), support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) and performance is measured by four parameters i.e., accuracy, precision, recall, f1 measure. We achieved 71% accuracy in Aspect Term task, 61% in Aspect Category, 74% in Aspect Term Polarity and 80% in Aspect Category Polarity The evaluation results depict that there is need to do more work in this task. We provided baseline evaluation for researcher community to further compare their results of ABSA systems.