Social media has seen a boom in recent years, with its users growing worldwide. The ubiquitous nature of social media has made it a useful tool to extract useful information regarding disasters. As in case of any disaster, people upload data related to the disaster on social media which can be very useful in the […]
Islamophobia or anti-Muslim antagonism is one prepotent yet dilapidated form of racism in our today’s world. The last couple of years have witnessed an immense surge in Islamophobic hate speech on social media nurturing and progressing violence and prejudice against Muslims and Islam. A growingly frequent expression of online hate speech is multimodal (text + […]
This thesis investigates the dynamics of crowdfunding success in Kickstarter’s technology category using a blend of network analysis and machine learning. By sourcing a comprehensive dataset from Webrobots.io, which includes detailed monthly data dumps of Kickstarter projects, this study focuses on United States (U.S.)-based technology projects to uncover patterns and factors contributing to successful crowdfunding […]
Our approach involves a fusion of deformable style transfer (DST), an optimization-centric technique that harmonizes the texture and geometry of a given content image to closely align with a chosen style image. This methodology is coupled with diffusion model inpainting, which is applied to content images originating from Calliar: an online dataset featuring handwritten Arabic calligraphy. Diverging from previous generative art methodologies, our approach presents aesthetically pleasing results, all the while preserving the integrity and structure of the Arabic script.
Recently social media has been a widely used network that connects people around the world. Not only this but people sharing their life events, thoughts through posts, status updates all gather up as a big data resource. This resource is helpful in conducting various researches, analyses including big data and machine learning. In this study, we analyzed six mental health issues using Reddit’s data. The data obtained summarizes; Depression, Anxiety, Bipolar, Bipolar Disorder, Schizophrenia, Autism and Mental Health which is a general class which discusses mental health. Experimentation is done using various deep learning and NLP techniques applied for classification such as Convolutional Neural Network, Long-short term memory network, Gated Recurrent Unit, Bi- Long-short term memory network and Bi-Gated Recurrent Unit. In addition to these traditional techniques, pre-trained BERT model and …
This research proposes a machine learning model and a deep learning model to predict the personality of an individual based on Myers–Briggs Type Indicator (MBTI) personality model. The proposed machine learning models (SVM, LR, MLP and XGBoost) were trained on MBTI and MBTI500 datasets with imbalanced and balanced instances (using SMOTE). The proposed deep learning model was trained using CNN with GloVe word embeddings. SVM model achieved the highest accuracy of 96.81% for machine learning model on MBTI500 dataset with SMOTE. However, CNN exhibited the highest accuracy of 99.54% on MBTI dataset which supersedes the existing models
This paper presents an approach towards bi-lingual sentiment analysis of tweets. Social networks being most advanced and popular communication medium can help in designing better government and business strategies. There are a number of studies reported that use data from social networks; however, most of them are based on English language. In this research, we […]
This paper presents a framework to perform concept- and keyword-based English search of the Holy Quran. The concept-based search is challenging as it should be able to handle the abstract queries which often do not occur verbatim in text. In order to implement it, a Qur’anic English WordNet is implemented, which is a WordNet-derived database […]
In this research, we present a novel approach to enhance Text Classification task using improved word representations that embed linguistic information (i.e. POS Tags). Word2vec is the vector based word representation, can be manipulated to work in conjunction with vector based POS representations to improve the accuracy of classifier in text classification task. we will […]
Detecting hate speech is indispensable irrespective of the scale of use of language as it inflicts huge harm on society. This work presents a first resource for classifying the severity of hate speech besides classifying offensive and hate speech content. Current research mostly limits hate speech classification only to its primary categories such as racism, […]