Open Access
Issue
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
Article Number 03001
Number of page(s) 11
Section Image Processing and Computer Vision
DOI https://doi.org/10.1051/itmconf/20257003001
Published online 23 January 2025
  1. Denis Eka Cahyani and I. Patasik, “Performance comparison of TF-IDF and Word2Vec models for emotion text classification,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 5, pp. 2780–2788, 2024, Accessed: Aug. 15, 2024. [Online]. Available: https://www.beei.org/index.php/EEI/article/view/3157/2341. [Google Scholar]
  2. S. Singh, K. Kumar, and B. Kumar, “Sentiment Analysis of Twitter Data Using TF- IDF and Machine Learning Techniques,” 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), May 2022, doi: https://doi.org/10.1109/com-it-con54601.2022.9850477. [Google Scholar]
  3. Ram Krishn Mishra, Siddhaling Urolagin, and A. Arul, “A Sentiment analysis-based hotel recommendation using TF-IDF Approach,” 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), IEEE Xplore, Dec. 2019, doi: https://doi.org/10.1109/iccike47802.2019.9004385. [Google Scholar]
  4. S. Rizal, Adiwijaya, and M. D. Purbolaksono, “Sentiment Analysis on Movie Review from Rotten Tomatoes Using Word2Vec and Naive Bayes,” 2022 1st International Conference on Software Engineering and Information Technology (ICoSEIT), IEEE Xplore, Nov. 01, 2022. https://ieeexplore.ieee.org/document/10030009. [Google Scholar]
  5. S. Manna and H. Nakai, “Effectiveness of Word Embeddings on Classifiers: A Case Study with Tweets,” 2019 IEEE 13th International Conference on Semantic Computing (ICSC), IEEE Xplore, Jan. 2019, doi: https://doi.org/10.1109/icosc.2019.8665538. [Google Scholar]
  6. Farhan Wahyu Kurniawan and Warih Maharani, “Indonesian Twitter Sentiment Analysis Using Word2Vec,” 2020 International Conference on Data Science and Its Applications (ICoDSA), IEEE Xplore, Aug. 2020, doi: https://doi.org/10.1109/icodsa50139.2020.9212906. [Google Scholar]
  7. B. Liu, “Text sentiment analysis based on CBOW model and deep learning in big data environment,” Journal of Ambient Intelligence and Humanized Computing, Oct. 2018, doi: https://doi.org/10.1007/s12652-018-1095-6. [Google Scholar]
  8. Y. HaCohen-Kerner, D. Miller, and Y. Yigal, “The influence of preprocessing on text classification using a bag-of-words representation,” PLOS ONE, vol. 15, no. 5, p. e0232525, May 2020, doi: https://doi.org/10.1371/journal.pone.0232525. [CrossRef] [Google Scholar]
  9. “Understanding TF-IDF (Term Frequency-Inverse Document Frequency),” GeeksforGeeks, Jan. 20, 2021. https://www.geeksforgeeks.org/understanding-tf-idf-term-frequency-inverse-document-frequency/ [Google Scholar]
  10. “Word Embeddings in NLP,” GeeksforGeeks, Oct. 11, 2020. https://www.geeksforgeeks.org/word-embeddings-in-nlp/ [Google Scholar]
  11. “Papers with Code - Skip-gram Word2Vec Explained,” Paperswithcode.com, 2020. https://paperswithcode.com/method/skip-gram-word2vec [Google Scholar]
  12. Z. Jaadi, “A Step-by-Step Explanation of Principal Component Analysis,” Built-In, Feb. 23, 2024. https://builtin.com/data-science/step-step-explanation-principal-component-analysis [Google Scholar]
  13. L. Rydin Gorjão, G. Hassan, J. Kurths, and D. Witthaut, “MFDFA: Efficient multifractal detrended fluctuation analysis in python,” Computer Physics Communications, p. 108254, Dec. 2021, doi: https://doi.org/10.1016/j.cpc.2021.108254. [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.