Acceso abierto

Empirical Analysis of Supervised and Unsupervised Machine Learning Algorithms with Aspect-Based Sentiment Analysis


Cite

The Hindu, “Abrogation of Article 370 led to breakdown of law and order in J&K,” 2020. [Online]. Available: https://www.thehindu.com/news/cities/Visakhapatnam/abrogation-of-article-370-led-to-breakdown-of-law-and-order-in-jk/article30669954.ece. (Accessed on: 26 June 2020). Search in Google Scholar

S. Bhat, “J&K administration ends house arrest of political leaders in Jammu,” Feb. 2022. [Online]. Available: https://www.indiatoday.in/india/story/j-k-administration-ends-house-arrest-of-political-leaders-in-jammu-1605412-2019-10-02 Search in Google Scholar

The Hindu, “Left parties protest amendment to Article 370, vow to continue fighting,” Aug. 2019. [Online]. Available: https://www.thehindu.com/news/national/left-parties-protest-scrapping-of-article-370-vow-to-continue-the-fight/article28825167.ece. Search in Google Scholar

Y. Dang, Y. Zhang, and H. Chen, “A lexicon-enhanced method for sentiment classification,” IEEE Intell. Syst., vol .25, no. 4, pp. 46–53, Nov. 2010. https://doi.org/10.1109/MIS.2009.105 Search in Google Scholar

M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, “Lexicon-based methods for sentiment analysis,” Comput. Linguist., vol. 37, no. 2, pp. 267–307, Jun. 2011. https://doi.org/10.1162/COLI_a_00049 Search in Google Scholar

“AFINN sentiment lexicon.” [Online]. Available: http://corpustext.com/reference/sentiment_afinn.html. Search in Google Scholar

P. Pandey, “Simplifying sentiment analysis using VADER in Python (on social media text),” Sep. 2018. [Online]. Available: https://medium.com/analytics-vidhya/simplifyingsocial-media-sentiment-analysis-using-vader-in-python-f9e6ec6fc52f. (Accessed on: 24 March, 2020). Search in Google Scholar

S. Mishra, “Unsupervised learning and data clustering,” May 2017. [Online]. Available: https://towardsdatascience.com/unsupervised-learning-and-data-clustering-eeecb78b422a. (Accessed on: 24 March, 2020). Search in Google Scholar

A. Khatua, K. Ghosh, and N. Chaki, “Can#Twitter_Trends predict election results? Evidence from 2014 Indian General Election,” in 48th Hawaii International Conference on System Sciences, Kauai, HI, USA, Jan. 2015, pp. 1676–1685. https://doi.org/10.1109/HICSS.2015.202 Search in Google Scholar

L. K. Hansen, A. Arvidsson, F. A. Nielsen, E. Colleoni, and M. Etter, “Good friends, bad news: Affect and virality in Twitter,” in Future Information Technology. Communications in Computer and Information Science, J. H. Park, L. T. Yang, and C. Lee, Eds., vol. 185. Springer, Berlin, Heidelberg, 2011, pp. 34–43. https://doi.org/10.1007/978-3-642-22309-9_5 Search in Google Scholar

M. Hu and B. Liu, “Mining and summarizing customer reviews,” in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA, Aug. 2004, pp. 168–177. https://doi.org/10.1145/1014052.1014073 Search in Google Scholar

R. Prabowo and M. Thelwall, “Sentiment analysis: A combined approach,” Journal of Informetrics, vol. 3, no. 2, pp. 143–157, Apr. 2009. https://doi.org/10.1016/j.joi.2009.01.003 Search in Google Scholar

S. Saha, J. Yadav, and P. Ranjan, “Sarcasm detection in twitter,” Indian J. Sci. Technol., vol. 10, no. 25, pp. 1–8, 2017. https://doi.org/10.17485/ijst/2017/v10i25/114443 Search in Google Scholar

A. Kumar and S. Singh, “Fake news detection of Indian and United States election data using machine learning algorithm,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 11, pp. 1559–1563, Sep. 2019. https://doi.org/10.35940/ijitee.K1829.0981119 Search in Google Scholar

P. Sharma and T.-S. Moh, “Prediction of Indian election using sentiment analysis on Hindi Twitter,” in 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, USA, Dec. 2016, pp. 1966–1971. https://doi.org/10.1109/BigData.2016.7840818 Search in Google Scholar

M. Wang and H. Shi, “Research on sentiment analysis technology and polarity computation of sentiment words,” in IEEE International Conference on Progress in Informatics and Computing, Shanghai, China, Dec. 2010, pp. 331–334. https://doi.org/10.1109/PIC.2010.5687438 Search in Google Scholar

P. Singh, & R. S. Sawhney, and K. S. Kahlon, “Sentiment analysis of demonetization of 500 & 1000 rupee banknotes by Indian government,” ICT Express, vol. 4, no. 3, pp. 124–129, Sep. 2017. https://doi.org/10.1016/j.icte.2017.03.001 Search in Google Scholar

M. Maragoudakis, E. Loukis, and Y. Charalabidis, “A review of opinion mining methods for analyzing citizens’ contributions in public policy debate,” in Electronic Participation. ePart 2011. Lecture Notes in Computer Science, E. Tambouris, A. Macintosh, and H. de Bruijn, Eds., vol 6847. Springer, Berlin, Heidelberg, 2011, pp. 298–313. https://doi.org/10.1007/978-3-642-23333-3_26 Search in Google Scholar

Devitt A and K. Ahmad, “Sentiment polarity identification in financial news,” in 45th Annual Meeting of the Association of Computational Linguistics, Prague, Czech Republic, Jun. 2007, pp. 984–991. https://aclanthology.org/P07-1124/ Search in Google Scholar

B. Liu and L. Zhang, “A survey of opinion mining and sentiment analysis,” in Mining Text Data, C. Aggarwal and C. Zhai, Eds. Springer, Boston, MA, 2012, pp. 415–463. https://doi.org/10.1007/978-1-4614-3223-4_13 Search in Google Scholar

V. Pankaj and J. Sanjay, “Mining public opinion on Indian government policies using R,” International Journal of Innovative Technology and Exploring Engineering, vol 9, no. 3, pp. 1310–1315, Jan. 2020. https://doi.org/10.35940/ijitee.C8150.019320 Search in Google Scholar

A. Hasan, S. Moin, A. Karim, and S. Shamshirband, “Machine learning-based sentiment analysis for Twitter accounts,” Mathematical and Computational Applications, vol. 23, no. 1, Feb. 2018, Art. no. 11. https://doi.org/10.3390/mca23010011 Search in Google Scholar

P. Rao, “Fine-grained sentiment analysis in Python (Part-1),” Towards Data Science, Sep. 2019. [Online]. Available: https://towardsdatascience.com/fine-grained-sentiment-analysis-in-python-part-1-2697bb111ed4 Search in Google Scholar

Developer Platform, “Twitter Rest API. [Online]. Available: https://developer.twitter.com/en/search-results?limit=10&offset=0&q=Twitter%20Rest%20API&searchPath=%2Fcontent%2Fdeveloper-twitter%2Fen&sort=relevance. Search in Google Scholar

P. Turney, “Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews,” in Meeting on Association for Computational Linguistics, Philadelphia, USA, Jul. 2002, pp. 417–424. https://arxiv.org/ftp/cs/papers/0212/0212032.pdf Search in Google Scholar

J. Brownlee, “What is a confusion matrix in machine learning,” Aug. 2020. [Online]. Available: https://machinelearningmastery.com/confusion-matrix-machine-learning/ Search in Google Scholar

N. Al Shammari and A. Al Mansour, “Aspect-based sentiment analysis and location detection for Arabic language Tweets,” Applied Computer Systems, vol. 27, no. 2, pp. 119–127, Dec. 2022. https://doi.org/10.2478/acss-2022-0013 Search in Google Scholar

eISSN:
2255-8691
Idioma:
Inglés
Calendario de la edición:
2 veces al año
Temas de la revista:
Computer Sciences, Artificial Intelligence, Information Technology, Project Management, Software Development