Sentiment Analysis from YouTube Video Using Bi-LSTM-GRU Classification-Reference-Cited by-同舟云学术

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Sentiment Analysis from YouTube Video Using Bi-LSTM-GRU Classification

Published:2024 Issue: Volume: Page:303-316
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:en
Short-container-title:

Author:

Hasan Firoz,Raza Dewan Mamun,Moon Hasan,Nahid Md. Aynul Hasan

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-8937-9_21

Reference24 articles.

1. Li G, Zheng Q, Zhang L, Guo S, Niu L (2020) Sentiment information based model for Chinese text sentiment analysis. In: 2020 IEEE 3rd international conference on automation, electronics and electrical engineering (AUTEEE), Shenyang, China, pp 366–371. https://doi.org/10.1109/AUTEEE50969.2020.9315668

2. Yue W, Li L (2020) Sentiment analysis using Word2vec-CNN-BiLSTM classification. In: 2020 Seventh international conference on social networks analysis, management and security (SNAMS), Paris, France, pp 1–5. https://doi.org/10.1109/SNAMS52053.2020.9336549

3. Woldemariam Y (2016) Sentiment analysis in a cross-media analysis framework. In: 2016 IEEE international conference on big data analysis (ICBDA), Hangzhou, China, pp 1–5. https://doi.org/10.1109/ICBDA.2016.7509790

4. Fan X, Li X, Du F, Li X, Wei M (2016) Apply word vectors for sentiment analysis of APP reviews. In: 2016 3rd international conference on systems and informatics (ICSAI), Shanghai, China, pp 1062–1066. https://doi.org/10.1109/ICSAI.2016.7811108

5. Ikoro V, Sharmina M, Malik K, Batista-Navarro R (2018) Analyzing sentiments expressed on Twitter by UK energy company consumers. In: 2018 Fifth international conference on social networks analysis, management and security (SNAMS), Valencia, Spain, pp 95–98. https://doi.org/10.1109/SNAMS.2018.8554619

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