Visual Sentiment Analysis Using Machine Learning for Entertainment Applications

Sayal, Anu; N, Chaithra; Jha, Janhvi; Trilochan, Balu; Kalyan, Gaduputi Venkata; Priya, M. Shravani; Gupta, Veethika; Memoria, Minakshi; Gupta, Ashulekha

doi:10.1109/icicat57735.2023.10263629

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Cited by 2 publications

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Real-Time Automated Detection of Errors in the Product Labels Using Image Processing and OCR

H N,

Kiran,

Kotiyal

et al. 2023

2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE)

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Real-Time Automated Detection of Errors in the Product Labels Using Image Processing and OCR

H N,

Kiran,

Kotiyal

et al. 2023

2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE)

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Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models

Pokhrel,

Dahal,

Rimal

et al. 2024

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Deep-SDM is a unified layer framework built on TensorFlow/Keras and written in ingPython 3.12. The framework aligns with the modular engineering principles for the design and development strategy. Transparency, reproducibility, and recombinability are the framework’s primary design criteria. The platform can extract valuable insights from numerical and text data and utilize them to predict future values by implementing long short-term memory (LSTM), gated recurrent unit (GRU), and convolution neural network (CNN). Its end-to-end machine learning pipeline involves a sequence of tasks, including data exploration, input preparation, model construction, hyperparameter tuning, performance evaluations, visualization of results, and statistical analysis. The complete process is systematic and carefully organized, from data import to model selection, encapsulating it into a unified whole. The multiple subroutines work together to provide a user-friendly and conducive pipeline that is easy to use. We utilized the Deep-SDM framework to predict the Nepal Stock Exchange (NEPSE) index to validate its reproducibility and robustness and observed impressive results.

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Visual Sentiment Analysis Using Machine Learning for Entertainment Applications

Cited by 2 publications

References 30 publications

Real-Time Automated Detection of Errors in the Product Labels Using Image Processing and OCR

Real-Time Automated Detection of Errors in the Product Labels Using Image Processing and OCR

Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models

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