Sentiment Analysis of Twitter Data Using Big Data Analytics and Deep Learning Model

Vanam, Harika; R, Jeberson Retna Raj

doi:10.1109/iceconf57129.2023.10084281

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Cited by 3 publications

References 15 publications

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Cross-Lingual Sentiment Analysis in Deep Learning: A Comparative Study of Multilingual Approaches

Kumar,

Singh

et al. 2023

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Cross-Lingual Sentiment Analysis in Deep Learning: A Comparative Study of Multilingual Approaches

Kumar,

Singh

et al. 2023

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Analyzing Large-Scale Twitter Real Time Streaming Data with Manifold Machine Learning Algorithms in Apache SPARK

Abhineswari,

Priyadarshini

2023

2023 International Conference on Data Science, Agents &Amp; Artificial Intelligence (ICDSAAI)

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A real-time predicting online tool for detection of people’s emotions from Arabic tweets based on big data platforms

Abdelhady,

Elsemman,

Soliman

2024

J Big Data

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Emotion prediction is a subset of sentiment analysis that aims to extract emotions from text, speech, or images. The researchers posit that emotions determine human behavior, making the development of a method to recognize emotions automatically crucial for use during global crises, such as the COVID-19 pandemic. In this paper, a real-time system is developed that identifies and predicts emotions conveyed by users in Arabic tweets regarding COVID-19 into standard six emotions based on the big data platform, Apache Spark. The system consists of two main stages: (1) Developing an offline model and (2) Online emotion prediction pipeline. For the first stage, two different approaches: The deep Learning (DL) approach and the Transfer Learning-based (TL) approach to find the optimal classifier for identifying and predicting emotion. For DL, three classifiers are applied: Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). For TL, five models are applied: AraBERT, ArabicBERT, ARBERT, MARBERT, and QARiB. For the second stage, create a Transmission Control Protocol (TCP) socket between Twitter’s API and Spark used to receive streaming tweets and Apache Spark to predict the label of tweets in real-time. The experimental results show that the QARiB model achieved the highest Jaccard accuracy (65.73%), multi-accuracy (78.71%), precision-micro (78.71%), recall-micro (78.71%), f-micro (78.71%), and f-macro (78.55%). The system is available as a web-based application that aims to provide a real-time visualization of people’s emotions during a crisis.

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Sentiment Analysis of Twitter Data Using Big Data Analytics and Deep Learning Model

Cited by 3 publications

References 15 publications

Cross-Lingual Sentiment Analysis in Deep Learning: A Comparative Study of Multilingual Approaches

Cross-Lingual Sentiment Analysis in Deep Learning: A Comparative Study of Multilingual Approaches

Analyzing Large-Scale Twitter Real Time Streaming Data with Manifold Machine Learning Algorithms in Apache SPARK

A real-time predicting online tool for detection of people’s emotions from Arabic tweets based on big data platforms

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