A transformer-based generative adversarial learning to detect sarcasm from Bengali text with correct classification of confusing text.

Sarcasm detection research in Bengali is still limited due to a lack of relevant resources. In this context, getting high-quality annotated data is costly and time-consuming. Therefore, in this paper, we present a transformer-based generative adversarial learning for sarcasm detection from Bengali text based on available limited labeled data. Here, we use the Bengali sarcasm dataset 'Ben-Sarc'. Besides, we construct another dataset containing Bengali sarcastic and non-sarcastic comments from YouTube and newspapers to observe the model's performance on the new dataset. On top of that, we utilize another Bengali sarcasm dataset 'BanglaSarc' to further prove our models' robustness. Among all models, the Bangla BERT-based Generative Adversarial Model has achieved the highest accuracy with 77.1% for the 'Ben-Sarc' dataset. Besides, this model has achieved the highest accuracy of 68.2% for the dataset constructed from YouTube and newspaper, and 97.2% for the 'BanglaSarc' dataset.

Linking Tweets Towards Geo-Localized Policies: COVID-19 Perspective.

COVID-19 pandemic is taking a toll on the social, economic, and psychological well-being of people. During this pandemic period, people have utilized social media platforms (e.g., Twitter) to communicate with each other and share their concerns and updates. In this study, we analyzed nearly 25M COVID-19 related tweets generated from 20 different countries and 28 states of USA over a month. We leveraged sentiment analysis and topic modeling over this collection and clustered different geolocations based on their sentiment. Our analysis identified 3 geo-clusters (country- and US state-based) based on public sentiment and discovered 15 topics that could be summarized under three main themes: government actions, medical issues, and people's mood during the home quarantine. The proposed computational pipeline has adequately captured the Twitter population's emotion and sentiment, which could be linked to government/policy makers' decisions and actions (or lack thereof). We believe that our analysis pipeline could be instrumental for the policymakers in sensing the public emotion/support with respect to the interventions/actions taken, for example, by the government instrumentality.

Multiobjective Formulation of Multiple Sequence Alignment for Phylogeny Inference.

Multiple sequence alignment (MSA) is a preliminary task for estimating phylogenies. It is used for homology inference among the sequences of a set of species. Generally, the MSA task is handled as a single-objective optimization process. The alignments computed under one criterion may be different from the alignments generated by other criteria, inferring discordant homologies and thus leading to different hypothesized evolutionary histories relating the sequences. The multiobjective (MO) formulation of MSA has recently been advocated by several researchers, to address this issue. An MO approach independently optimizes multiple (often conflicting) objective functions at the same time and outputs a set of competitive alignments. However, no conceptual or experimental rational from a real-world application perspective has been reported so far for any MO formulation of MSA. This article work investigates the impact of MO formulation in the context of an important scientific problem, namely, phylogeny estimation. Employing popular evolutionary MO algorithms, we show that: 1) trees inferred based on alignments produced by the existing MSA methods used in practice are substantially worse in quality than the trees inferred based on the alignment's output by an MO algorithm and 2) even high-quality alignments (according to popular measures available in the literature) may fail to achieve acceptable accuracy in generating phylogenetic trees. Thus, we essentially ask the following natural question: "can a phylogeny-aware (i.e., application-aware) metric guide in selecting appropriate MO formulations to ensure better phylogeny estimation?" Here, we report a carefully designed extensive experimental study that positively answers this question.