Sentiment analysis is a vast area in the Machine learning domain. A lot of work is done on datasets and their analysis of the English Language. In Pakistan, a huge amount of data is in roman Urdu language, it is scattered all over the social sites including Twitter, YouTube, Facebook and similar applications. In this study the focus domain of dataset gathering is YouTube comments. The Dataset contains the comments of people over different Pakistani dramas and TV shows. The Dataset contains multi-class classification that is grouped The comments into positive, negative and neutral sentiment. In this Study comparative analysis is done for five supervised learning Algorithms including linear regression, SVM, KNN, Multi layer Perceptron and Naïve Bayes classifier. Accuracy, recall, precision and F-measure are used for measuring performance. Results show that accuracy of SVM is 64 percent, which is better than the rest of the list.
The understanding of the human language is quantified by identifying intents and entities. Even though classification methods that rely on labeled information are often used for the comprehension of language understanding, it is incredibly time consuming and tedious process to generate high propensity supervised datasets. In this paper, we present the generation of accurate intents for the corresponding Roman Urdu unstructured data and integrate this corpus in RASA NLU module for intent classification. We embed knowledge graph with RASA Framework to maintain the dialog history for semantic bas…
In present century, data volume is increasing enormously. The data could be in form for image, text, voice, and video. One factor in this huge growth of data is usage of social media where everyone is posting data on daily basis during chatting, exchanging information, and uploading their personal and official credential. Research of sentiments seeks to uncover abstract knowledge in Published texts in which users communicate their emotions and thoughts about shared content, including blogs, news and social networks. Roman Urdu is the one of most dominant language on social networks in Pakistan…
National University of Computer and Emerging Sciences, FAST（国家计算机与新兴科学大学FAST校区）
Code-switching is a common phenomenon among people with diverse lingual background and is widely used on the internet for communication purposes. In this paper, we present a Recurrent Neural Network combined with the Attention Model for Language Identification in Code-Switched Data in English and low resource Roman Urdu. The attention model enables the architecture to learn the important features of the languages hence classifying the code switched data. We demonstrated our approach by comparing the results with state of the art models i.e. Hidden Markov Models, Conditional Random Field and Bi…
In recent studies, it has been shown that Multilingual language models underperform their monolingual counterparts. It is also a well-known fact that training and maintaining monolingual models for each language is a costly and time-consuming process. Roman Urdu is a resource-starved language used popularly on social media platforms and chat apps. In this research, we propose a novel dataset of scraped tweets containing 54M tokens and 3M sentences. Additionally, we also propose RUBERT a bilingual Roman Urdu model created by additional pretraining of English BERT. We compare its performance wit…
Comsats University of Information Technology Islamabad, Pakistan (Abbottabad Campus)（巴基斯坦伊斯兰堡 COMSATS 信息技术大学（阿伯塔巴德校区））；University of Science and Technology of China (USTC), Hefei, People Republic of China（中国科学技术大学（合肥））；Near East University Cyprus KKTC（塞浦路斯近东大学（北塞浦路斯土耳其共和国））
Now a day computer is necessary for human being and it is very useful in many fields like search engine, text processing, short messaging services, voice chatting and text recognition. Since last many years there are many tools and techniques that have been developed to support the writing of language script. Most of the Asian languages like Arabic, Urdu, Persian, Chains and Korean are written in Roman alphabets. Roman alphabets are the most commonly used for transliteration of languages, which have non-Latin scripts. For writing Urdu characters as an input, there are many layouts which are al…
Pretrained language models are now of widespread use in Natural Language Processing. Despite their success, applying them to Low Resource languages is still a huge challenge. Although Multilingual models hold great promise, applying them to specific low-resource languages e.g. Roman Urdu can be excessive. In this paper, we show how the code-switching property of languages may be used to perform cross-lingual transfer learning from a corresponding high resource language. We also show how this transfer learning technique termed Bilingual Language Modeling can be used to produce better performing…
A Clustering Framework for Lexical Normalization of Roman Urdu
Stevens Institute of Technology（史蒂文斯理工学院）；City University of New York（纽约城市大学）；Lahore University of Management Sciences（拉合尔管理科学大学）；Qatar Computing Research Institute（卡塔尔计算研究所）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Information Technology University（信息技术大学）
Punjab University College of Information Technology（旁遮普大学信息技术学院）；University of the Punjab（旁遮普大学）
Hate speech is a specific type of controversial content that is widely legislated as a crime that must be identified and blocked. However, due to the sheer volume and velocity of the Twitter data stream, hate speech detection cannot be performed manually. To address this issue, several studies have been conducted for hate speech detection in European languages, whereas little attention has been paid to low-resource South Asian languages, making the social media vulnerable for millions of users. In particular, to the best of our knowledge, no study has been conducted for hate speech detection i…
University of Helsinki（赫尔辛基大学）；Rochester Institute of Technology（罗切斯特理工学院）；University of Wolverhampton（伍尔弗汉普顿大学）
This paper describes the submissions by team HWR to the Dravidian Language Identification (DLI) shared task organized at VarDial 2021 workshop. The DLI training set includes 16,674 YouTube comments written in Roman script containing code-mixed text with English and one of the three South Dravidian languages: Kannada, Malayalam, and Tamil. We submitted results generated using two models, a Naive Bayes classifier with adaptive language models, which has shown to obtain competitive performance in many language and dialect identification tasks, and a transformer-based model which is widely regarde…
