Zitieren

[1] J. Serrano-Guerrero, J. A. Olivas, F. P. Romero, and E. Herrera-Viedma, “Sentiment analysis: A review and comparative analysis of web,” Information Sciences, vol. 311, pp. 18–38, Aug. 2015. https://doi.org/10.1016/j.ins.2015.03.040 Search in Google Scholar

[2] L. Zhang, S. Wang, and B. Liu, “Deep learning for sentiment analysis: A survey,” WIRES data mining and knowledge discovery, vol. 8, no. 4, July 2018. https://doi.org/10.1002/widm.1253 Search in Google Scholar

[3] M. Giatsogloua, M. G. Vozalis, K. Diamantaras, A. Vakali, G. Sarigiannidis, and K. C. Chatzisavvas, “Sentiment analysis leveraging emotions and word embeddings,” Expert Systems with Applications, vol. 69, pp. 214–224, Mar. 2017. https://doi.org/10.1016/j.eswa.2016.10.043 Search in Google Scholar

[4] K. K. Mohbey, B. Bakariya, and V. Kalal, “A study and comparison of sentiment analysis techniques using demonetization: Case study,” in Sentiment Analysis and Knowledge Discovery in Contemporary Business, 2018, pp. 1–14. https://doi.org/10.4018/978-1-5225-4999-4.ch001 Search in Google Scholar

[5] C. S. Khoo and S. B. Johnkhan, “Lexicon-based sentiment analysis: Comparative Evaluation of Six Sentiment Lexicons,” Journal of Information Science, vol. 44, no. 4, pp. 491–511, 19 Apr. 2017. https://doi.org/10.1177/0165551517703514 Search in Google Scholar

[6] N. Boudad, R. Faizi, R. O. Haj Thami, and R. Chiheb, “Sentiment analysis in Arabic: A review of the literature,” Ain Shams Engineering Journal, vol. 9, no. 4, pp. 2479–2490, Dec. 2018. https://doi.org/10.1016/j.asej.2017.04.007 Search in Google Scholar

[7] S. Tartir and I. A. Nabi, “Semantic sentiment analysis in Arabic social media,” Journal of King Saud University – Computer and Information Sciences, vol. 29, no. 2, pp. 229–223, Apr. 2017. https://doi.org/10.1016/j.jksuci.2016.11.011 Search in Google Scholar

[8] A. K. Rathore, V. Ilavarasan, and Y. K. Dwivedi, “Social media content and product co-creation: An emerging paradigm,” Journal of Enterprise Information Management, vol. 29, no. 1, pp. 7–18, Feb. 2016. https://doi.org/10.1108/JEIM-06-2015-0047 Search in Google Scholar

[9] J. L. Sheela, “A review of sentiment analysis in Twitter data using Hadoop,” International Journal of Database Theory and Application, vol. 9, no. 1, pp. 77–86, 2016. https://doi.org/10.14257/ijdta.2016.9.1.07 Search in Google Scholar

[10] S. A. Salloum, M. Al-Emran, A. A. Monem, and K. Shaalan, “A survey of text mining in social media: Facebook and Twitter perspectives,” Advances in Science, Technology and Engineering Systems, vol. 2, no. 1, pp. 127–133, 2017. https://doi.org/10.25046/aj020115 Search in Google Scholar

[11] “Twitter launches,” A&E Television Networks, 14 July 2020. [Online]. Available: https://www.history.com/this-day-in-history/twitter-launches. Accessed on: Aug. 2020. Search in Google Scholar

[12] “Number of monetizable daily active Twitter users (mDAU) worldwide from 1st quarter 2017 to 2nd quarter 2020,” 23 July 2020. [Online]. Available: https://www.statista.com/statistics/970920/monetizable-daily-active-twitter-users-worldwide/. Accessed on: Aug. 2020. Search in Google Scholar

[13] Y. Lin, “10 Twitter statistics every marketer should know in 2022 [infographic],” 30 July 2019. [Online]. Available: https://www.oberlo.com/blog/twitter-statistics. Accessed on: Oct. 2019. Search in Google Scholar

[14] D. Hattem and L. Lomicka, “What the Tweets say: A critical analysis of Twitter research in language learning from 2009 to 2016,” E-Learning and Digital Media, vol. 13, pp. 5–23, Oct. 2019. https://doi.org/10.1177/2042753016672350 Search in Google Scholar

[15] Twitter Inc., “Twitter for websites-supported languages,” 2019. [Online]. Available: https://developer.twitter.com/en/docs/twitter-forwebsites/twitter-for-websites-supported-languages/overview. Accessed on: 2019. Search in Google Scholar

[16] H. B. Zaya, A. A. Raza, and A. Ather, “Urdu word segmentation using conditional random fields (CRFs),” in Proceedings of the 27th International Conference on Computational Linguistics, Santa Fe, New Mexico: Association for Computational Linguistics, 2018, pp. 2562–2569. Search in Google Scholar

[17] V. S. Pagolu, K. N. R. Challa, and G. Panda, “Sentiment analysis of Twitter data for predicting stock market movements,” in International conference on Signal Processing, Communication, Power and Embedded System, Paralakhemundi, India, Oct. 2016, pp. 1345–1350. https://doi.org/10.1109/SCOPES.2016.7955659 Search in Google Scholar

[18] R. P. Schumaker, A. T. Jarmoszko, and J. L. S. Chester, “Predicting wins and spread in the Premier League using a sentiment analysis of twitter,” Decision Support Systems, vol. 88, pp. 76–84, Aug. 2016. https://doi.org/10.1016/j.dss.2016.05.010 Search in Google Scholar

[19] D. Pope and J. Griffith, “An analysis of online Twitter sentiment surrounding the European,” in 8th International Conference on Knowledge Discovery and Information Retrieval, Porto, Portugal, 2016, pp. 299–306. https://doi.org/10.5220/0006051902990306 Search in Google Scholar

[20] A. C. Pandey, D. S. Rajpoot, and M. Saraswat, “Twitter sentiment analysis using hybrid cuckoo search method,” Information Processing & Management, vol. 53, no. 4, pp. 764–779, July 2017. https://doi.org/10.1016/j.ipm.2017.02.004 Search in Google Scholar

[21] H. K. Aldayel and A. M. Azmi, “Arabic tweets sentiment analysis – a hybrid scheme,” Journal of Information Science, vol. 42, no. 6, pp. 782–797, Oct. 2016. https://doi.org/10.1177/0165551515610513 Search in Google Scholar

[22] A. M. Alayba, V. Palade, M. England, and R. Iqbal, “Arabic language sentiment analysis on health services,” in 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR), Nancy, France, Apr. 2017, pp. 114–118. https://doi.org/10.1109/ASAR.2017.8067771 Search in Google Scholar

[23] M. Heikal, M. Torki, and N. El-Makky, “Sentiment analysis of Arabic Tweets using deep learning,” Procedia Computer Science, vol. 142, pp. 114–122, 2018. https://doi.org/10.1016/j.procs.2018.10.466 Search in Google Scholar

[24] A. Hassan, 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. https://doi.org/10.3390/mca23010011 Search in Google Scholar

[25] I. Javed, H. Afzal, A. Majeed, and B. Khan, “Towards creation of linguistic resources for bilingual sentiment analysis of Twitter data,” in International Conference on Applications of Natural Language to Data Bases/Information Systems, Jun. 2018. https://doi.org/10.1007/978-3-319-07983-7_32 Search in Google Scholar

[26] S. Ahmed, S. Hina, and R. Asif, “Detection of sentiment polarity of unstructured multi-language text from social media,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 7, pp. 199–203, 2019. https://doi.org/10.14569/IJACSA.2018.090728 Search in Google Scholar

[27] T. R. Soomro and S. M. Ghulam, “Current status of urdu on Twitter,” Sukkur IBA Journal of Computing and Mathematical Sciences, vol. 3, no. 1, pp. 28–33, 2019. https://doi.org/10.30537/sjcms.v3i1.397 Search in Google Scholar

[28] F. Noor, M. Bakhtyar, and J. Baber, “Sentiment analysis in E-commerce using SVM on Roman Urdu text,” in International Conference for Emerging Technologies in Computing, Jul. 2019. https://doi.org/10.1007/978-3-030-23943-5_16 Search in Google Scholar

[29] H. Ghulam, F. Zeng, W. Li, and Y. Xiao, “Deep learning-based sentiment analysis for Roman Urdu text,” in 2018 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2018, vol. 147, 2018, pp. 131–135. https://www.sciencedirect.com/journal/procedia-computer-science/vol/147/suppl/C10.1016/j.procs.2019.01.202 Search in Google Scholar

[30] Z. Mehmood et al., “Deep sentiments in Roman Urdu text using recurrent convolutional neural network model,” Information Processing and Management, vol. 57, no. 4, Feb. 2020, Art no. 102233. https://doi.org/10.1016/j.ipm.2020.102233 Search in Google Scholar

[31] V. Bonta, N. Kumaresh, and J. N, “A comprehensive study on lexicon based approaches for sentiment analysis,” Asian Journal of Computer Science and Technology, vol. 8, no. S2, pp. 1–6, Mar. 2019. https://doi.org/10.51983/ajcst-2019.8.S2.2037 Search in Google Scholar

[32] S. Sarica and J. Luo, “Stopwords in technical language processing”, PLoS ONE, vol. 16, no. 8, Aug. 2021, Art no. e0254937. https://doi.org/10.1371/journal.pone.0254937834161534351911 Search in Google Scholar

[33] K. S. Dar, A. B. Shafat, and H. U. Muhammad, “An efficient stop word elimination algorithm for Urdu language,” in 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Phuket, Thailand, Jun. 2017. https://doi.org/10.1109/ECTICon.2017.8096386 Search in Google Scholar

[34] M. Usman, S. Ayub, Z. Shafique, and K. Malik, “Urdu text classification using majority voting,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 8, pp. 265–273, 2016. https://doi.org/10.14569/IJACSA.2016.070836 Search in Google Scholar

[35] K. Riaz and D. Becker, “Stopword identification in an Urdu corpus”. Search in Google Scholar

[36] A. Burney, B. Sami, N. Mahmood, Z. Abbas, and K. Rizwan, “Urdu text summarizer using sentence weight algorithm for word processors,” International Journal of Computer Applications, vol. 46, no. 19, pp. 38–43, May 2012. Search in Google Scholar

[37] E. D. P. Kaur and E. P. Singh, “A comparative research of rule based classification on dataset using WEKA TOOL,” International Research Journal of Engineering and Technology (IRJET), vol. 6, no. 9, Sep. 2019. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.irjet.net/archives/V6/i9/IRJET-V6I9345.pdf Search in Google Scholar

[38] R. Ahujaa, A. Chuga, S. Kohlia, S. Guptaa, and P. Ahuja, “The impact of features extraction on the sentiment analysis,” in International Conference on Pervasive Computing Advances and Applications, vol. 152, 2019, pp. 341–348. https://www.sciencedirect.com/journal/procedia-computer-science/vol/152/suppl/C10.1016/j.procs.2019.05.008 Search in Google Scholar

[39] B. Stecanella, “What is TF-IDF?” May 2019. [Online]. Available: https://monkeylearn.com/blog/what-is-tf-idf/. Accessed on: July 2020. Search in Google Scholar

[40] S. Gnanambal, M. Thangaraj, V. T. Meenatchi, and V. Gayathri, “Classification algorithms with attribute selection: an evaluation study using WEKA,” International Journal of Advanced Networking and Applications, vol. 9, no. 6, pp. 3640–3644, May 2018. Search in Google Scholar

[41] M. Desai and M. A. Mehta, “Techniques for sentiment analysis of Twitter data: A comprehensive survey,” in International Conference on Computing, Communication and Automation, Greater Noida, India, Apr. 2016, pp. 149–154. https://doi.org/10.1109/CCAA.2016.7813707 Search in Google Scholar

[42] S. Yıldırım, “How to best evaluate a classification model,” 17 March 2020. [Online]. Available: https://towardsdatascience.com/how-to-best-evaluate-a-classification-model-2edb12bcc587. Search in Google Scholar

[43] P. Subedi, “Machine learning – The different ways to evaluate your classification models and choose the best one!” 18 August 2020. [Online]. Available: https://medium.com/kharpann/machine-learning-the-different-ways-to-evaluate-your-classification-models-and-choose-the-best-1281542432c. Accessed on: July 2020. Search in Google Scholar

[44] M. Ghosh and G. Sanyal, “An ensemble approach to stabilize the features for multi-domain sentiment analysis using supervised machine learning,” Journal of Big Data, vol. 5, Nov. 2018, Art no. 44. https://doi.org/10.1186/s40537-018-0152-5 Search in Google Scholar

[45] V. Chaurasia and S. Pal, “A novel approach for breast cancer detection using data mining techniques,” International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization), vol. 2, no. 1, pp. 2456–2465, Jul. 2017. https://www.researchgate.net/publication/259979477_A_Novel_Approach_for_Breast_Cancer_Detection_using_Data_Mining_Techniques Search in Google Scholar

[46] Y. A. Amrani, M. Lazaar, and K. E. E. Kadiri, “Random forest and support vector machine based hybrid approach to sentiment analysis,” in The First International Conference on Intelligent Computing in Data Sciences, vol. 127, 2018, pp. 511–520. https://www.sciencedirect.com/journal/procedia-computer-science/vol/127/suppl/C10.1016/j.procs.2018.01.150 Search in Google Scholar

[47] M. A. Fauzi, “Random forest approach for sentiment analysis in Indonesian language,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 12, no. 1, pp. 46–50, Oct. 2018. https://doi.org/10.11591/ijeecs.v12.i1.pp46-50 Search in Google Scholar

eISSN:
2255-8691
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
2 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Informatik, Künstliche Intelligenz, Informationstechnik, Projektmanagement, Softwareentwicklung