Search Results for author: Arunkumar Bagavathi

Found 15 papers, 1 papers with code

Exploiting Adaptive Contextual Masking for Aspect-Based Sentiment Analysis

no code implementations21 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

HeTriNet: Heterogeneous Graph Triplet Attention Network for Drug-Target-Disease Interaction

no code implementations30 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}).

Drug Discovery

Modeling Political Orientation of Social Media Posts: An Extended Analysis

no code implementations21 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.

Few-Shot Learning

Learning Unbiased News Article Representations: A Knowledge-Infused Approach

no code implementations12 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.

Quantitative Analysis of Forecasting Models:In the Aspect of Online Political Bias

no code implementations11 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.

Misinformation Time Series +1

Scalable Pathogen Detection from Next Generation DNA Sequencing with Deep Learning

no code implementations30 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.

Representation Learning

Metagenome2Vec: Building Contextualized Representations for Scalable Metagenome Analysis

no code implementations9 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.

Representation Learning

A Machine Learning Pipeline to Examine Political Bias with Congressional Speeches

no code implementations18 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.

BIG-bench Machine Learning

MG-NET: Leveraging Pseudo-Imaging for Multi-Modal Metagenome Analysis

no code implementations21 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.

Representation Learning

Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models

no code implementations24 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.

Network Embedding

ragamAI: A Network Based Recommender System to Arrange a Indian Classical Music Concert

no code implementations8 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.

Recommendation Systems

Examining Untempered Social Media: Analyzing Cascades of Polarized Conversations

no code implementations10 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.

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