Bridging Emotions and Architecture: Sentiment Analysis in Modern Distributed Systems

• URL: http://arxiv.org/abs/2503.18260v1
• Date: Mon, 24 Mar 2025 01:01:19 GMT
• Title: Bridging Emotions and Architecture: Sentiment Analysis in Modern Distributed Systems
• Authors: Mahak Shah, Akaash Vishal Hazarika, Meetu Malhotra, Sachin C. Patil, Joshit Mohanty,
• Abstract summary: Sentiment analysis is applied in various areas such as; social media surveillance, customer feedback evaluation and market research.<n>This paper examines how sentiment analysis converges with distributed systems by concentrating on different approaches, challenges and future investigations.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Sentiment analysis is a field within NLP that has gained importance because it is applied in various areas such as; social media surveillance, customer feedback evaluation and market research. At the same time, distributed systems allow for effective processing of large amounts of data. Therefore, this paper examines how sentiment analysis converges with distributed systems by concentrating on different approaches, challenges and future investigations. Furthermore, we do an extensive experiment where we train sentiment analysis models using both single node configuration and distributed architecture to bring out the benefits and shortcomings of each method in terms of performance and accuracy.

Related papers

• Sentiment Analysis in SemEval: A Review of Sentiment Identification Approaches [1.6874375111244329]
  We study top-ranking systems for each SemEval edition during the 2013-2021 period.<n>We analyze the proposed systems marking the evolution of research trends with a focus on the main components of sentiment analysis systems.<n>Our study shows an active use of preprocessing techniques, an evolution of features engineering and word representation from lexicon-based approaches to word embeddings, and the dominance of neural networks and transformers.
  arXiv  Detail & Related papers  (2025-03-13T15:25:23Z)
• Domain Adaptation and Entanglement: an Optimal Transport Perspective [86.24617989187988]
  Current machine learning systems are brittle in the face of distribution shifts (DS), where the target distribution that the system is tested on differs from the source distribution used to train the system.<n>For deep neural networks, a popular framework for unsupervised domain adaptation (UDA) is domain matching, in which algorithms try to align the marginal distributions in the feature or output space.<n>In this paper, we derive new bounds based on optimal transport that analyze the UDA problem.
  arXiv  Detail & Related papers  (2025-03-11T08:10:03Z)
• Out-of-Distribution Detection on Graphs: A Survey [58.47395497985277]
  Graph out-of-distribution (GOOD) detection focuses on identifying graph data that deviates from the distribution seen during training.<n>We categorize existing methods into four types: enhancement-based, reconstruction-based, information propagation-based, and classification-based approaches.<n>We discuss practical applications and theoretical foundations, highlighting the unique challenges posed by graph data.
  arXiv  Detail & Related papers  (2025-02-12T04:07:12Z)
• GCM-Net: Graph-enhanced Cross-Modal Infusion with a Metaheuristic-Driven Network for Video Sentiment and Emotion Analysis [2.012311338995539]
  This paper presents a novel framework that leverages the multi-modal contextual information from utterances and applies metaheuristic algorithms to learn for utterance-level sentiment and emotion prediction. To show the effectiveness of our approach, we have conducted extensive evaluations on three prominent multimodal benchmark datasets.
  arXiv  Detail & Related papers  (2024-10-02T10:07:48Z)
• Multilayer Environment and Toolchain for Holistic NetwOrk Design and Analysis [2.7763199324745966]
  This work analyses in detail the requirements for distributed systems assessment. Our approach emphasizes setting up and assessing a broader spectrum of distributed systems. We demonstrate the framework's capabilities to provide valuable insights across various use cases.
  arXiv  Detail & Related papers  (2023-10-24T21:18:25Z)
• Deep Learning-based Analysis of Basins of Attraction [49.812879456944984]
  This research addresses the challenge of characterizing the complexity and unpredictability of basins within various dynamical systems. The main focus is on demonstrating the efficiency of convolutional neural networks (CNNs) in this field.
  arXiv  Detail & Related papers  (2023-09-27T15:41:12Z)
• Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition [107.58227666024791]
  Face recognition systems are widely deployed in safety-critical applications, including law enforcement. They exhibit bias across a range of socio-demographic dimensions, such as gender and race. Previous works on bias mitigation largely focused on pre-processing the training data.
  arXiv  Detail & Related papers  (2022-10-18T15:46:05Z)
• Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review [62.997667081978825]
  This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
  arXiv  Detail & Related papers  (2022-04-29T08:06:05Z)
• DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup [58.894901088797376]
  federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. We propose a general framework to solve the above two challenges simultaneously. We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
  arXiv  Detail & Related papers  (2022-04-16T08:08:29Z)
• A Machine-Learning-Aided Visual Analysis Workflow for Investigating Air Pollution Data [18.972547412113567]
  Analyzing air pollution data is challenging as there are various analysis focuses from different aspects: feature (what), space (where), and time (when) We develop a visual analytic system that supports a flexible analysis workflow, allowing domain experts to freely explore different aspects based on their analysis needs.
  arXiv  Detail & Related papers  (2022-02-11T02:24:21Z)
• Interpretable Multi-dataset Evaluation for Named Entity Recognition [110.64368106131062]
  We present a general methodology for interpretable evaluation for the named entity recognition (NER) task. The proposed evaluation method enables us to interpret the differences in models and datasets, as well as the interplay between them. By making our analysis tool available, we make it easy for future researchers to run similar analyses and drive progress in this area.
  arXiv  Detail & Related papers  (2020-11-13T10:53:27Z)
• Interpretable collaborative data analysis on distributed data [9.434133337939498]
  This paper proposes an interpretable non-model sharing collaborative data analysis method as one of the federated learning systems. By centralizing intermediate representations, which are individually constructed in each party, the proposed method obtains an interpretable model. Numerical experiments indicate that the proposed method achieves better recognition performance for artificial and real-world problems than individual analysis.
  arXiv  Detail & Related papers  (2020-11-09T13:59:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.