Open Access
Issue
E3S Web Conf.
Volume 472, 2024
International Conference on Renewable Energy, Green Computing and Sustainable Development (ICREGCSD 2023)
Article Number 01017
Number of page(s) 11
Section Smart and Energy Efficient Systems
DOI https://doi.org/10.1051/e3sconf/202447201017
Published online 05 January 2024
  1. Sentiment140: This dataset contains 1.6 million tweets that have been manually labeled as positive or negative. The dataset can be downloaded from here: http://help.sentiment140.com/for-students/ [Google Scholar]
  2. Twitter Sentiment Analysis Dataset: This dataset contains 1, 578, 627 tweets that have been labeled as positive, negative, or neutral. The dataset can be downloaded from here: https://www.kaggle.com/kazanova/sentiment140 [Google Scholar]
  3. SemEval-2017 Task 4: This dataset contains 10, 000 tweets that have been labeled as positive, negative, or neutral. The dataset can be downloaded from here: https://competitions.codalab.org/competitions/16380#learnthedetails [Google Scholar]
  4. Stanford Sentiment Treebank: This dataset contains 215, 154 phrases that have been labeled as positive or negative. The dataset can be downloaded from here: https://nlp.stanford.edu/sentiment/treebank.html [Google Scholar]
  5. Kaggle Twitter US Airline Sentiment: This dataset contains tweets about US airlines, labeled as positive, negative or neutral. The dataset can be downloaded from: https://www.kaggle.com/crowdflower/twitter-airline-sentiment. [Google Scholar]
  6. Afroz, S., Satyamurty, C.V.S., Asifa Tazeem, P., Hanimi Reddy, M., Riyazuddin, Y.M., Jadda, V. (2023). Dynamic Twitter Topic Summarization Using Speech Acts. In: Morusupalli, R., Dandibhotla, T.S., Atluri, V.V., Windridge, D., Lingras, P., Komati, V.R. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2023. Lecture Notes in Computer Science, Vol. 14078. Springer, Cham. [Google Scholar]
  7. Detection and classification of trendy topics for recommendation based on Twitter data on different genre, Indira, D.N.V.S.L.S., Kiran Kumar, R., Prasad, G.V.S.N.R.V., Usha Rani, R. Smart Innovation, Systems and Technologies, 2019, 105, pp. 143–153. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.