no code implementations • 21 Feb 2024 • S M Rafiuddin, Mohammed Rakib, Sadia Kamal, Arunkumar Bagavathi
Further, we show that the proposed methods can be extended with multiple adaptations and demonstrate a qualitative analysis of the proposed approach using sample text for aspect term extraction.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2
no code implementations • 30 Nov 2023 • Farhan Tanvir, Khaled Mohammed Saifuddin, Tanvir Hossain, Arunkumar Bagavathi, Esra Akbas
To address these challenges, we effectively model the interconnectedness of all entities in a heterogeneous graph and develop a novel Heterogeneous Graph Triplet Attention Network (\texttt{HeTriNet}).
no code implementations • 21 Nov 2023 • Sadia Kamal, Brenner Little, Jade Gullic, Trevor Harms, Kristin Olofsson, Arunkumar Bagavathi
We conduct experiments using the proposed heuristic methods and machine learning approaches to predict the political orientation of posts collected from two social media forums with diverse political ideologies: Gab and Twitter.
no code implementations • 12 Sep 2023 • Sadia Kamal, Jimmy Hartford, Jeremy Willis, Arunkumar Bagavathi
Quantification of the political leaning of online news articles can aid in understanding the dynamics of political ideology in social groups and measures to mitigating them.
no code implementations • 11 Sep 2023 • Srinath Sai Tripuraneni, Sadia Kamal, Arunkumar Bagavathi
Understanding and mitigating political bias in online social media platforms are crucial tasks to combat misinformation and echo chamber effects.
no code implementations • 30 Nov 2022 • Sai Narayanan, Sathyanarayanan N. Aakur, Priyadharsini Ramamurthy, Arunkumar Bagavathi, Vishalini Ramnath, Akhilesh Ramachandran
The emergence of zoonotic diseases from novel pathogens, such as the influenza virus in 1918 and SARS-CoV-2 in 2019 that can jump species barriers and lead to pandemic underscores the need for scalable metagenome analysis.
no code implementations • 9 Nov 2021 • Sathyanarayanan N. Aakur, Vineela Indla, Vennela Indla, Sai Narayanan, Arunkumar Bagavathi, Vishalini Laguduva Ramnath, Akhilesh Ramachandran
There is an increased need for learning robust representations from metagenome reads since pathogens within a family can have highly similar genome structures (some more than 90%) and hence enable the segmentation and identification of novel pathogen sequences with limited labeled data.
no code implementations • 18 Sep 2021 • Prasad hajare, Sadia Kamal, Siddharth Krishnan, Arunkumar Bagavathi
Computational methods to model political bias in social media involve several challenges due to heterogeneity, high-dimensional, multiple modalities, and the scale of the data.
no code implementations • 21 Jul 2021 • Sathyanarayanan N. Aakur, Sai Narayanan, Vineela Indla, Arunkumar Bagavathi, Vishalini Laguduva Ramnath, Akhilesh Ramachandran
However, there are significant challenges in developing such an approach, the chief among which is to learn self-supervised representations that can help detect novel pathogen signatures with very low amounts of labeled data.
1 code implementation • 3 Nov 2020 • Joshua Melton, Arunkumar Bagavathi, Siddharth Krishnan
- and the lack of baseline models for fringe outlets such as Gab.
no code implementations • 24 Jul 2020 • Sai Narayanan, Akhilesh Ramachandran, Sathyanarayanan N. Aakur, Arunkumar Bagavathi
Bovine Respiratory Disease Complex (BRDC) is a complex respiratory disease in cattle with multiple etiologies, including bacterial and viral.
BIG-bench Machine Learning Cultural Vocal Bursts Intensity Prediction +3
no code implementations • 24 Jul 2020 • Michael Ridenhour, Arunkumar Bagavathi, Elaheh Raisi, Siddharth Krishnan
We also analyze a multilayer network, constructed from two types of user interactions in Gab(quote and reply) and interaction scores from the weak supervision model as edge weights, to predict hateful users.
no code implementations • 30 Jan 2020 • Sathyanarayanan N. Aakur, Arunkumar Bagavathi
Egocentric perception has grown rapidly with the advent of immersive computing devices.
no code implementations • 8 Dec 2019 • Arunkumar Bagavathi, Siddharth Krishnan, Sanjay Subrahmanyan, S. L. Narasimhan
1) it will assist musicians to customize their performance with the necessary variety required to sustain the interest of the audience for the entirety of the concert 2) it will generate carefully curated lists of south Indian classical music so that the listener can discover the wide range of melody that the musical system can offer.
no code implementations • 10 Jun 2019 • Arunkumar Bagavathi, Pedram Bashiri, Shannon Reid, Matthew Phillips, Siddharth Krishnan
Online social media, periodically serves as a platform for cascading polarizing topics of conversation.