Application of Traditional and Deep Learning Algorithms in Sentiment Analysis of Global Warming Tweets-Reference-Cited by-同舟云学术

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Application of Traditional and Deep Learning Algorithms in Sentiment Analysis of Global Warming Tweets

Published:2024 Issue: Volume: Page:49-61
ISSN:1867-8211
Container-title:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
language:en
Short-container-title:

Author:

Nikolova Dragana,Mircheva Georgina,Zdravevski Eftim^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-52524-7_4

Reference21 articles.

1. Climate change. https://www.kaggle.com/datasets/edqian/twitter-climate-changesentiment-dataset

2. English pipeline. https://spacy.io/models/en

3. Global emissions. https://www.c2es.org/content/international-emissions

4. Internet slang. https://www.internetslang.com/

5. Azzouza, N., Akli-Astouati, K., Oussalah, A., Bachir, S.A.: A real-time twitter sentiment analysis using an unsupervised method. In: Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, pp. 1–10 (2017)

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data;IEEE Access;2024

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