| 1. |
- Batra, Rakhi, et al.
(author)
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Evaluating Polarity Trend Amidst the Coronavirus Crisis in Peoples's Attitudes toward the Vaccination Drive
- 2021
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In: Sustainability. - : MDPI. - 2071-1050. ; 13:10
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Journal article (peer-reviewed)abstract
- It has been more than a year since the coronavirus (COVID-19) engulfed the whole world, disturbing the daily routine, bringing down the economies, and killing two million people across the globe at the time of writing. The pandemic brought the world together to a joint effort to find a cure and work toward developing a vaccine. Much to the anticipation, the first batch of vaccines started rolling out by the end of 2020, and many countries began the vaccination drive early on while others still waiting in anticipation for a successful trial. Social media, meanwhile, was bombarded with all sorts of both positive and negative stories of the development and the evolving coronavirus situation. Many people were looking forward to the vaccines, while others were cautious about the side-effects and the conspiracy theories resulting in mixed emotions. This study explores users's tweets concerning the COVID-19 vaccine and the sentiments expressed on Twitter. It tries to evaluate the polarity trend and a shift since the start of the coronavirus to the vaccination drive across six countries. The findings suggest that people of neighboring countries have shown quite a similar attitude regarding the vaccination in contrast to their different reactions to the coronavirus outbreak.
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| 2. |
- Ghafoor, Abdul, et al.
(author)
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SentiUrdu-1M : A large-scale tweet dataset for Urdu text sentiment analysis using weakly supervised learning
- 2023
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 18:8
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Journal article (peer-reviewed)abstract
- Low-resource languages are gaining much-needed attention with the advent of deep learning models and pre-trained word embedding. Though spoken by more than 230 million people worldwide, Urdu is one such low-resource language that has recently gained popularity online and is attracting a lot of attention and support from the research community. One challenge faced by such resource-constrained languages is the scarcity of publicly available large-scale datasets for conducting any meaningful study. In this paper, we address this challenge by collecting the first-ever large-scale Urdu Tweet Dataset for sentiment analysis and emotion recognition. The dataset consists of a staggering number of 1,140,821 tweets in the Urdu language. Obviously, manual labeling of such a large number of tweets would have been tedious, error-prone, and humanly impossible; therefore, the paper also proposes a weakly supervised approach to label tweets automatically. Emoticons used within the tweets, in addition to SentiWordNet, are utilized to propose a weakly supervised labeling approach to categorize extracted tweets into positive, negative, and neutral categories. Baseline deep learning models are implemented to compute the accuracy of three labeling approaches, i.e., VADER, TextBlob, and our proposed weakly supervised approach. Unlike the weakly supervised labeling approach, the VADER and TextBlob put most tweets as neutral and show a high correlation between the two. This is largely attributed to the fact that these models do not consider emoticons for assigning polarity.
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| 3. |
- Imran, Ali Shariq, et al.
(author)
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The impact of synthetic text generation for sentiment analysis using GAN based models
- 2022
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In: Egyptian Informatics Journal. - : Elsevier. - 1110-8665. ; 23:3, s. 547-557
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Journal article (peer-reviewed)abstract
- Data imbalance in datasets is a common issue where the number of instances in one or more categories far exceeds the others, so is the case with the educational domain. Collecting feedback on a course on a large scale and the lack of publicly available datasets in this domain limits models' performance, especially for deep neural network based models which are data hungry. A model trained on such an imbalanced dataset would naturally favor the majority class. However, the minority class could be critical for decision-making in prediction systems, and therefore it is usually desirable to train a model with equally high class-level accuracy. This paper addresses the data imbalance issue for the sentiment analysis of users' opinions task on two educational feedback datasets utilizing synthetic text generation deep learning models. Two state-of-the-art text generation GAN models namely CatGAN and SentiGAN, are employed for synthesizing text used to balance the highly imbalanced datasets in this study. Particular emphasis is given to the diversity of synthetically generated samples for populating minority classes. Experimental results on highly imbalanced datasets show significant improvement in models' performance on CR23K and CR100K after balancing with synthetic data for the sentiment classification task.
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| 4. |
- Sayers, Dave, et al.
(author)
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The Dawn of the Human-Machine Era : A forecast of new and emerging language technologies
- 2021
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Reports (other academic/artistic)abstract
- New language technologies are coming, thanks to the huge and competing private investment fuelling rapid progress; we can either understand and foresee their effects, or be taken by surprise and spend our time trying to catch up. This report scketches out some transformative new technologies that are likely to fundamentally change our use of language. Some of these may feel unrealistically futuristic or far-fetched, but a central purpose of this report - and the wider LITHME network - is to illustrate that these are mostly just the logical development and maturation of technologies currently in prototype. But will everyone benefit from all these shiny new gadgets? Throughout this report we emphasise a range of groups who will be disadvantaged and issues of inequality. Important issues of security and privacy will accompany new language technologies. A further caution is to re-emphasise the current limitations of AI. Looking ahead, we see many intriguing opportunities and new capabilities, but a range of other uncertainties and inequalities. New devices will enable new ways to talk, to translate, to remember, and to learn. But advances in technology will reproduce existing inequalities among those who cannot afford these devices, among the world’s smaller languages, and especially for sign language. Debates over privacy and security will flare and crackle with every new immersive gadget. We will move together into this curious new world with a mix of excitement and apprehension - reacting, debating, sharing and disagreeing as we always do. Plug in, as the human-machine era dawns.
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| 5. |
- Shaikh, Sarang, et al.
(author)
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Towards Improved Classification Accuracy on Highly Imbalanced Text Dataset Using Deep Neural Language Models
- 2021
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In: Applied Sciences. - : MDPI. - 2076-3417. ; 11:2, s. 1-20
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Journal article (peer-reviewed)abstract
- Data imbalance is a frequently occurring problem in classification tasks where the number of samples in one category exceeds the amount in others. Quite often, the minority class data is of great importance representing concepts of interest and is often challenging to obtain in real-life scenarios and applications. Imagine a customers’ dataset for bank loans-majority of the instances belong to non-defaulter class, only a small number of customers would be labeled as defaulters, however, the performance accuracy is more important on defaulters labels than non-defaulter in such highly imbalance datasets. Lack of enough data samples across all the class labels results in data imbalance causing poor classification performance while training the model. Synthetic data generation and oversampling techniques such as SMOTE, AdaSyn can address this issue for statistical data, yet such methods suffer from overfitting and substantial noise. While such techniques have proved useful for synthetic numerical and image data generation using GANs, the effectiveness of approaches proposed for textual data, which can retain grammatical structure, context, and semantic information, has yet to be evaluated. In this paper, we address this issue by assessing text sequence generation algorithms coupled with grammatical validation on domain-specific highly imbalanced datasets for text classification. We exploit recently proposed GPT-2 and LSTM-based text generation models to introduce balance in highly imbalanced text datasets. The experiments presented in this paper on three highly imbalanced datasets from different domains show that the performance of same deep neural network models improve up to 17% when datasets are balanced using generated text.
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