Open Access
Issue |
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
|
|
---|---|---|
Article Number | 01048 | |
Number of page(s) | 12 | |
DOI | https://doi.org/10.1051/e3sconf/202343001048 | |
Published online | 06 October 2023 |
- “Twitter” https://www.britannica.com/topic/Twitter [Google Scholar]
- “What is Sentiment Analysis?” https://monkeylearn.com/sentiment-analysis/ [Google Scholar]
- “Sentiment Analysis: Types, Tools and Use Cases” https://www.altexsoft.com/blog /business/sentiment-analysis-types-tools-and-use-cases [Google Scholar]
- “Twitter Sentiment Analysis: What it is + steps to follow” https://www.questionpro .com/blog/twitter-sentiment-analysis/ [Google Scholar]
- A. Tumasjan, T. Sprenger, P. Sandner, & I. Welpe, Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment, in Proceedings of the International AAAI Conference on Web and Social Media, ICWSM, 4, 1 (2010) [Google Scholar]
- O. Almatrafi, S. Parack, and B. Chavan, Application of location-based sentiment analysis using Twitter for identifying trends towards Indian general elections 2014, in Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication, IMCOM '15, 41, 1–5. (2015) [Google Scholar]
- Gupta and N. Joshi, IEEE Transactions on Computational Social Systems, 8(4), 917-927 , (2021) [Google Scholar]
- R.H. Ali, G. Pinto, E. Lawrie, E. J. Linstead. J Big Data 9, 79 (2022) [CrossRef] [PubMed] [Google Scholar]
- C. Hutto and E. Gilbert, VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text, in Proceedings of the International AAAI Conference on Web and Social Media, ICWSM, 8(1), 216-225, (2014) [CrossRef] [Google Scholar]
- V. Bonta, and N. Kumaresh and N. Janardhan. Asian J. Comp. Sci. Tech 8, S2 (2019) [Google Scholar]
- A. Kodirekka, & S. A, Intl. J. Res 5, 12 (2018) [Google Scholar]
- “Scrape Twitter data without Twitter API using SNScrape for timeseries analysis” https://datasciencedojo.com/blog/scrape-twitter-data-using-snscrape/ [Google Scholar]
- N. Sharma, V. Jain, Evaluation and summarization of student feedback using sentiment analysis, in Proceedings of the Advanced Machine Learning Technologies and Applications (AMLTA2020), Springer Singapore (2020) [Google Scholar]
- R. P. Ramkumar, P. Sanjeeva, Intl. J. Recent Tech Engg 7, 5 (2019) [Google Scholar]
- R. P. Ram Kumar, P. Sanjeeva, S. F. Lazarus, D. V. Krishna, Intl. J. Inno. Tech. Explor. Engg 8, 11S2 (2019) [Google Scholar]
- R. Boorugu, G. Ramesh, A Survey on NLP based Text Summarization for Summarizing Product Reviews, in Proceedings of the 2nd International Conference on Inventive Research in Computing Applications (ICIRCA2020), 9183355, (2020) [Google Scholar]
- M. N. Mohammad, Ch. U. Kumari, A. S. D. Murthy, B. O. L. Jagan, K. Saikumar, Mater. Today Proc 45 (2021) [Google Scholar]
- M. Thejaswee, V. Srilakshmi, K. Anuradha, G. Karuna, Performance Analysis of Machine Learning Algorithms for Text Classification, in Proceedings of the Advanced Informatics for Computing Research (ICAICR 2020), A. K. Luhach, D. S. Jat, K. H. Bin Ghazali, Gao, P. Lingras, (eds), Comm. Comp. Inform. Sci. Springer, Singapore 1393 (2021) [Google Scholar]
- M. Thejaswee, P. Srilakshmi, G. Karuna, K. Anuradha, Hybrid IG and GA based Feature Selection Approach for Text Categorization, in Proceedings of the 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, (2020) [Google Scholar]
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