Duygu Analizi ve Çeşitli İşletme Uygulamaları
Özet
Referanslar
Abbasi, A., Chen, H. ve Salem, A. (2008). Sentiment analysis in multiple languages. ACM Transactions on Information Systems, 26(3), 1-34.
Agarwal, B., Mittal, N., Bansal, P. ve Garg., S. (2015). Sentiment analysis using common-sense and context information. Hindawi Publishing Corporation Computational Intelligence and Neuroscience, 2015(6), 1-9.
Ahmed, A. A. A., Agarwal, S., Kurniawan, I. G. A., Anantadjaya, S. P. D. ve Krishman, C. (2022). Business boosting through sentiment analysis using Artificial Intelligence approach. International Journal of System Assurance Engineering and Management, 13, 699-709.
Ain, A. T., Ali, M., Riaz, A., Noureen, A., Kamran, M., Hayat, B. ve Rehman, A. (2017). Sentiment analysis using deep learning techniques: A review. International Journal of Advanced Computer Science and Applications, 8(6), 424-433.
Alaei, A. R., Becken, S. ve Stantic, B. (2017). Sentiment analysis in tourism: Capitalizing on big data. Journal of Travel Research, 1-7.
Alamoudi, E. S. ve Alghamdi, N. S. (2020). Sentiment classification and aspect-based sentiment analysis on yelp reviews using deep learning and word embeddings. Journal of Decision Systems, 1-23.
Al-Azzam, N. ve Shatnawi, I. (2021). Comparing supervised and semi-supervised machine learning models on diagnosing breast cancer. Annals of Medicine and Surgery, 62, 53-64.
Alhumoud, S. O., ve Al Wazrah, A. A. (2021). Arabic sentiment analysis using recurrent neural networks: A review. Artificial Intelligence Review. DOI:10.1007/s10462-021-09989-9.
Alzamzami, F., Hoda, M. ve El Saddık, A. (2020). Light gradient boosting machine for general sentiment classification on short texts: A comparative evaluation. IEEE Access, 8, 101840-101858.
Appel, O., Chiclana, F., Carter, J. ve Fujita, H. (2016). A hybrid approach to the sentiment analysis problem at the sentence level, Knowledge-Based Systems, 108, 110-124.
Arora, D., Li, K. F. ve Neville, S. W. (2015). Consumers’ sentiment analysis of popular phone brands and operating system preference using Twitter data: A feasibility study. IEEE 29th International Conference on Advanced Information Networking and Applications, 24-27 Mart, Gwangiu, Güney Kore.
Atan, S. ve Çınar, Y. (2019). Borsa İstanbul’da finansal haberler ile piyasa değeri ilişkisinin metin madenciliği ve duygu (sentiment) analizi ile incelenmesi. Ankara Üniversitesi SBF Dergisi, 74(1), 1-34.
Bahrainian, S.-A. ve Dengel, A. (2013). Sentiment analysis using sentiment features. 2013 IEEE/WIC/ACM International Conferences on Web Intelligence (WI) and Intelligent Agent Technology (IAT), 17-20 Kasım, Atlanta, GA, ABD, 26-29.
Balahur, A., Mihalcea, E. ve Montoyo, A. (2014). Computational approaches to subjectivity and sentiment analysis: Present and envisaged methods and applications. Computer Speech ve Language, 28(1), 1-6.
Balaji, T. K. ve Annavarapu, C. S. R. ve Bablani (2021). Machine learning algorithms for social media analysis: A survey. Computer Science Review, 40, 100395, 1-32.
Bartusiak, R., Augustyniak, L., Kajdanowicz, T. ve Kazienko, P. (2015). Sentiment analysis for polish using transfer learning approach. Second European Network Intelligence Conference, Eylül 21-22, Karlskrona, İsveç.
Behdenna, S., Barigou, F. ve Belalem, G. (2018). Document level sentiment analysis: A survey. EAI Endorsed Transactions on Context-aware Systems and Applications, 4(13), 1-8.
Bergsma, S., McNamee, P., Bagdouri, M., Fink, C. ve Wilson, T. (2012). Language identification for creating language-specific twitter collections. Proceedings of the 2012 Workshop on Language in Social Media (LSM 2012), Montreal, Kanada, Haziran 7, 65-74.
Bhadane, C., Dalal, H. ve Doshi, H. (2015). Sentiment analysis: Measuring opinions. Procedia Computer Science, 45, 808-814.
Birjali, M., Kasri, M. ve Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226:107134, 1-26.
Boiy, E. ve Moens, M-F. (2009). A machine learning approach to sentiment analysis in multilingual Web texts. Inf Retrieval, 12, 526-558.
Bose, R., Dey, R. K., Roy, S. ve Sarddar, D. D. (2020). Survey of twitter viewpoint on application of drugs by VADER sentiment analysis among distinct countries. Information and Communication Technology for Sustainable Development, 559-569.
Bouazizi, M. ve Ohtsuki, T. (2016). Sentiment analysis in Twitter: From classification to quantification of sentiments within tweets. IEEE Global Communications Conference (GLOBECOM), 04-08 Aralık, Washington, DC, ABD.
Cai, G. ve Xia, B. (2015). Convolutional Neural Networks for Multimedia Sentiment Analysis. Li, J., Ji, H., Zhao, D., Feng, Y. (Eds.). In Natural Language Processing and Chinese Computing. NLPCC 2015 2015. Lecture Notes in Computer Science, 9362. Springer, Cham.
Cambria, E. (2016). Affective computing and sentiment analysis. IEEE Intelligent Systems, 31(2), 102-107.
Caro, L. D. ve Grella, M. (2013). Sentiment analysis via dependency parsing. Computer Standards & Interfaces, 35, 442-453.
Chachra, A., Mehndiratta, P. ve Gupta, M. (2017). Sentiment analysis of text using deep convolution neural networks. Tenth International Conference on Contemporary Computing (IC3). 10-12 Ağustos, Noida, Hindistan.
Chaturvedi, S., Mishra, V. ve Mishra, N. (2017). Sentiment analysis using machine learning for business intelligence. IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), 21-22 Eylül, Chennai, Hindistan.
Chen, Y., Jiang, C., Wang, C.-Y., Gao, Y. ve Liu, J. K. R. (2015). Decision learning: Data analytic learning with strategic decision making. IEEE Signal Processing Magazine, 33(1), 34-56.
Cheng, Y., Yao, L., Xiang, G., Zhang, G., Thang, T. ve Zhong, L. (2020). Text sentiment orientation analysis based on multi-channel CNN and bidirectional GRU with attention mechanism. IEEE Access, 8, 134964- 134975.
Chitra, P., Karthik, T.S., Nithya, S., Jacinth Poornima, J., Srinivas Rao, J., Upadhyaya, M., Jayaram Kumar, K., Geethamani, R. ve Manjunath, T.C. (2020). Sentiment analysis of product feedback using natural language processing, 23, 1-4.
Ciocodeică, D.-F., Chivu, I.-C., Mihălcescu, H., Orzan, G., Băjan, A.-M. (2022). The degree of adoption of business ıntelligence in Romanian companies—The case of sentiment analysis as a marketing analytical tool. Sustainability, 14(7518), 1-20.
Collomb, A., Costea, C., D., Joyeux, Hasan, O. ve Brunie, L. (2013). A Study and comparison of sentiment analysis methods for reputation evaluation. Computer Science.
Cortes, C. ve Vapnik, V. (1995) Support-vector networks. Machine Learning, 20(3), 273-297.
Crawford, M., Khoshgoftaar, T. M., Prusa, J. D., Richter, A. N. ve Najada, H. A. (2015). Survey of review spam detection using machine learning techniques. Journal of Big Data, 2(23), 1-24.
D’Andrea, A., Ferri, F., Grifoni, P. ve Guzzo, T. (2015). Approaches, tools and applications for sentiment analysis implementation. International Journal of Computer Applications, 125(3), 26-33.
D’avanzo, E. ve Kuflik, T. (2013). E-commerce websites services versus buyers expectations: An empirıial analysis of the online marketplace. International Journal of Information Technology & Decision Making, 12(4), 651-677.
D’Avanzo, E. ve Pilato, G. (2014). Mining social network users opinions’ to aid buyers’ shopping decisions. Computers in Human Behavior, 1284-1295.
Desai, Z., Anklesaria, K. ve Balasubramaniam, H. (2021). Business Intelligence Visualization Using Deep Learning Based Sentiment Analysis on Amazon Review Data. IEEE 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 06-08 Temmuz, Kharagpur, Hindistan.
Devika, M. D., Sunitha, C. ve Ganesh, A. (2016). Sentiment analysis: A comparative study on different approaches. Procedia Computer Science, 87, 44-49.
Dey, L., Haque, M., Khurdiya, A. ve Shroff, G. (2017). Acquiring competitive ıntelligence from social media. Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data, 17 Eylül, Beijing, Çin.
Duwairi, R. M. ve Qarqaz, I. (2014). Arabic sentiment analysis using supervised classification. The 1st International Workshop on Social Networks Analysis, Management and Security (SNAMS - 2014), Ağustos 1-10, Barselona, İspanya.
Eisenstein, J. (2016). Unsupervised Learning for lexicon-based classification. arXiv:1611.06933.
Ficamos, P., Liu, Y. ve Chen, W. (2017). A Naive bayes and maximum entropy approach to sentiment analysis: Capturing domain-specific data in weibo. IEEE International Conference on Big Data and Smart Computing (BigComp), 13-16 Şubat, Jeju, Güney Kore, 336-339.
García-Pablos, A., Cuadros, M. ve Rigau, G. (2018). W2VLDA: Almost unsupervised system for aspect based sentiment analysis. Expert Systems With Applications, 91, 127-137.
Ghiassi, M., Skinner, J. ve Zimbra, D. (2013). Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with Applications, 40, 6266-6282.
Giatsoglou, M., Vozalis, M. G., Diamantaras, K., Vakali, Sarigiannidis, A. G. ve Chatzisavvas, K. C. (2017). Sentiment analysis leveraging emotions and word embeddings. Expert Systems With Applications, 69, 214-224.
Go, A., Bhayani, R. ve Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N project report, Stanford 1.
Gruzd, A., ve Haythornthwaite, C. (2013). Enabling community through social media. Journal of Medical Internet Research, 15(10), e248, 1-17.
Han, B. (2021). Comparison of different machine learning algorithms in classification. Journal of Physics: Conference Series, 2037, 1-5.
He, W., Shen, J., Tian, X., Li, Y., Akula, V., Yan, G. ve Tao, R. (2015). Gaining competitive intelligence from social media data. Industrial Management ve Data Systems, 115(9), 1622-1636.
He, W., Zha, S. ve Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33, 464-472.
Hemmatian, F. ve Sohrabi, M. K. (2019). A survey on classification techniques for opinion mining and sentiment analysis. Artificial Intelligence Review, 52(1), 1495-1545.
Hong, S., Lee, J. ve Lee, J-H. (2014). Competitive self-training technique for sentiment analysis in mass social media. Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS). 03-06 Aralık, Kitakyushu, Japonya, 9-12.
Hutto, C. J., ve Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings Of The 8th International Conference on Weblogs And Social Media, 8(1), 9-10.
Jain, A. ve Jain, M. (2017). Location based Twitter opinion mining using common-sense information. Global Journal of Enterprise Information System, 9(2), 28–32.
Jeyapriya, A. ve Selvi, K. (2015) Extracting aspects and mining opinions in product reviews using supervised learning algorithm. 2nd International conference on electronics and communication systems, 26-27 Şubat, Coimbatore, Hindistan. IEEE, 548–552.
Kechaou, Z., Ammar, M. B. ve Alimi, A. M. (2011). Improving e-learning with sentiment analysis of users’ opinions. Global Engineering Education Conference (EDUCON), 4- 6 Nisan, Ammani Jordan, 1032-1038.
Khan, A., Baharudin, B., Lee, L. H. ve Khan, K. (2010). A review of machine learning algorithms for text-documents classification. Journal Of Advances In Informatıon Technology, 1(1), 4-20.
Khan, S. (2022). Business intelligence aspect for emotions and sentiments analysis. First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), 16-18 Şubat, Trichy, Hindistan.
Kharde, V. A. ve Sonawane, S. S. (2016). Sentiment analysis of twitter data: A survey of techniques. International Journal of Computer Applications, 19(11), 5-15.
Kobayashi, N., Inui, K. ve Matsumoto, Y. (2007). Extracting aspect-evaluation and aspect-of relations in opinion mining. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 1065–1074, Haziran, Prag, Çek Cumhuriyeti, 1065-1074.
Kranjc, J., Smailović, J., Podpečan, V., Grčar, M., Žnidaršič, N. ve Lavrač, N. (2015). Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform. Information Processing and Management, 51, 187-203.
Kurnia, F. P. ve Suharjito. (2018). Business intelligence model to analyze social media information. Procedia Computer Science, 135, 5-14.
Lee, L. ve Pereira, F. (1999). Distributional similarity models: Clustering vs. nearest neighbors. Proceedings of the 37th Annual Meeting of the ACL, June, College Park, Maryland, ABD, 33-40.
Lee, H-j., Shin, H., Hwang, S-s., Cho, S. ve MacLachlan, D. (2010). Semi-supervised response modeling. Journal of Interactive Marketing, 24, 42–54.
Liang, B., Su, H., Gui, L., Camria, E. ve Xu, R. (2022). Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowledge-Based Systems, 235: 107643, 1-11.
Liu, B. (2011). Web data mining. Exploring hyperlinks, contents, and usage data. Springer: Chicago.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures On Human Language Technologies, 5(1), 1-167.
Madhoushi, Z., Hamdan, A. R. ve Zainudin, S. (2015). Sentiment analysis techniques in recent works. Science and Information Conference, Temmuz 28-30, Londra, İngiltere, 288-291.
Manek, A.S., Shenoy, P.D., Mohan, M.C. ve K.R., M. (2017). Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web, 20, 135–154.
Medhat, W., Hassan, A. ve Korashy, H. (2015). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113.
Micu, A., Micu, A. E., Geru, M. ve Lixandroiu, R. C. (2017). Analyzing user sentiment in social media: Implications for online marketing strategy. Psychology ve Marketing, 34(12), 1069-1134.
Muhammad, A., Wiratunga, N. ve Lothian, R. (2016). Contextual sentiment analysis for social media genres. Knowledge-Based Systems, 108, 92-101.
Nanli, Z., Ping., Z., Weiguo, L. ve Meng, C. (2012). Sentiment analysis: A literature review. International Symposium on Management of Technology (ISMOT), pp. 572–576
Nasreen Taj, M. B. ve Girisha, G. S. (2021). Insights of strength and weakness of evolving methodologies of sentiment analysis. Materials Today: Proceedings, 2, 157-162.
Neethu, M. S. ve Rajasree, R. (2013). Sentiment analysis in twitter using machine learning techniques. International Conference on Computing and Networking Technology (ICCNT), 04-06 Temmuz, Tiruchengode, Hindistan.
Pak, A. ve Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. Conference: Proceedings of the International Conference on Language Resources and Evaluation, LREC 2010, 17-23 Mayıs, Valletta, Malta, 1320-1326.
Pang, B. ve Lee, L. (2005). Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. Proceedings of the 43rd Annual Meeting of the ACL, Haziran, Ann Arbor, Michigan, 115-124.
Pang, B., ve Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Park, S. ve Kim, Y. (2016). Building thesaurus lexicon using dictionarybased approach for sentiment classification. IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA), 8-10 Haziran, Towson, MD, ABD.
Parveen, H. ve Pandey, S. (2016). Sentiment analysis on twitter data-set using naive bayes algorithm. 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 21-23 Temmuz, Karnataka, Hindistan.
Pathak, A. R., Pandey, M. ve Rautaray, S. (2021). Topic-level sentiment analysis of social media data using deep learning. Applied Soft Computing, 108, 1-17.
Peng, W. ve Park, D. H. (2011). Generate adjective sentiment dictionary for social media sentiment analysis using constrained nonnegative matrix factorization. Fifth International AAAI Conference on Weblogs and Social Media, 5(1), 273-180.
Pereira, D. A. (2020). A survey of sentiment analysis in the Portuguese language. Artificial Intelligence Review. DOI: 10.1007/s10462-020-09870-1
Poria, S., Cambria, E., Hazarika, D., Mazumder, N., Zadeh, A. ve Morency, L-P. (2017). Context-dependent sentiment analysis in user-generated videos. Proceedingsof the 55th Annual Meeting of the Association for Computational Linguistics, 30 Temmuz- 4 Ağustos, Vancouver, Kanada, 873-883.
Prakash, T. N. Ve Aloysius, A. (2020). Applications, approaches, and challenges in sentiment analysis (AACSA). International Research Journal of Modernization in Engineering Technology and Science, 2(7), 910-915.
Radhika, N. (2017). Music recommendation system based on user’s sentiment. Recent Innovations in Computational Intelligence and Image Processing, 23 Şubat, Kerala, Hindistan, 46-49.
Rahmath, H. P. ve Ahmed, T. (2014). Fuzzy based sentiment analysis of online product reviews using machine learning techniques. International Journal of Computer Applications, 99(17), 9-16.
Rokade, P. P. ve Kumari, A. (2019). Business intelligence analytics using sentiment analysis-a survey. International Journal of Electrical and Computer Engineering (IJECE), 9(1), 613-620.
Rosa, R. L., Rodríguez, D. Z. ve Bressan, G. (2015). Music recommendation system based on user’s sentiments extracted from social networks. IEEE Transactions on Consumer Electronics, 61(3), 359-367.
Saggion, H. ve Funk, A. (2009). Extracting opinions and facts for business intelligence. RNTI Journal, E (17), 119-146.
Saif, H., He, Y., Fernandez, M. ve Alani, H. (2016). Contextual semantics for sentiment analysis of Twitter. Information Processing and Management, 52, 5-19.
Šaloun, P., Hruzík, M. ve Zelinka, I. (2013). Sentiment analysis - e-bussines and e-learning common issue. 11th IEEE International Conference on Emerging eLearning Technologies and Applications, 24-25 Ekim, Stary Smokovec, The High Tatras, Slovakya, 339-343.
Salur, M. U. ve Aydın, İ. (2020). A novel hybrid deep learning model for sentiment classification. IEEE Access, (8), 58080-58093.
Samuels, A. ve Mcgonical, J. (2020). Sentiment analysis on social media content. arXiv:2007.02144.
Sánchez-Holgado, P., ve Arcila-Calderón, C. (2018). Towards the study of sentiment in the public opinion of science in Spanish. Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality - TEEM’18. Ekim,963–970, Salamanca, İspanya.
Saptal, M. S. ve Raut, R. (2021). Sentiment analysis in songs and advanced recommendation system. International Journal of Current Engineering and Technology, Özel Sayı 8.
Schuller, B., Mousa, A. E.-D., ve Vryniotis, V. (2015). Sentiment analysis and opinion mining: on optimal parameters and performances. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(5), 255–263.
Sharma, A. ve Ghose, U. (2020). Sentimental analysis of twitter data with respect to general elections in India. Procedia Computer Science, 173, 325-334.
Shivapvasad, T. K. ve Shetty, J. (2017). Sentiment analysis of product reviews: A review. International Conference on Inventive Communication and Computational Technologies (ICICCT 2017), 10-11 Mart, Coimbatore, Hindistan.
Smeureanu, I. ve Bucur, C. (2012). Applying supervised opinion mining techniques on online user reviews. Informatica Economică, 16(2), 81-91.
Sohrabi, M. K. ve Karimi, F. (2018). A feature selection approach to detect spam in the facebook social network. Arabian Journal for Science and Engineering, 43, 949-958.
Sreesurya, I., Rathi, H., Jain, P. ve Jain, T. K. (2020). Hypex: A tool for extracting business intelligence from sentiment analysis using enhanced LSTM. Multimedia Tools and Applications, 73, 35641-35663.
Stojanovski, D., Strezoski, G., Madjarov, G., Dimitrovski, I. ve Chorbev, I. (2018). Deep neural network architecture for sentiment analysis and emotion identification of Twitter messages. Multimedia Tools and Applications, 77, 32213-32242.
Sun, T., Wang, J., Zhang, P., Cao, Y., Liu, B. ve Wang, D. (2017). Predicting stock price returns using microblog sentiment for Chinese stock market. 3rd International Conference on Big Data Computing and Communications, 10-11 Ağustos, Chengdu, Çin.
Taherdoost, H. ve Madanchian, M. (2023). Artificial intelligence and sentiment analysis: A review in competitive research. Computers, 12(17), 1-15.
Tang, H., Tan, S. ve Cheng, X. (2009). A survey on sentiment detection of reviews. Expert Systems with Applications, 36, 10760-10773.
Teixeira, A. ve Laureano, R. M. S. (2017). Data extraction and preparation to perform a sentiment analysis using open source tools: The example of a Facebook fashion brand page. 12th Iberian Conference on Information Systems and Technologies (CISTI), 21-24 Haziran, Lizbon, Portekiz.
Thelwall, M., Wilkinson, D., ve Uppal, S. (2009). Data mining emotion in social network communication: Gender differences in MySpace. Journal of the American Society for Information Science and Technology, 61(1), 190–199. doi:10.1002/asi.21180
Thelwall, M., Buckley, K., ve Paltoglou, G. (2010). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406-418. doi:10.1002/asi.21462
Thet, T. T., Na, J.-C. ve Khoo, S. G. (2010). Aspect-based sentiment analysis of movie reviews on discussion boards. Journal of Information Science, 36(6), 823-848.
Torfi, A., Shirvani, R. A., Keneshloo, Y., Tavaf, N. ve Fox, E. A. (2020). Natural language processing advancements by deep learning: A survey. arXiv:2003.01200, 1-23.
Vafeiadis, T., Diamantaras, K., Sarigiannidis, G. ve Chatzisavvas, K. (2015), A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory, 55, 1-9.
Vashisht, G. ve Jailia, M. (2019). Panoptical view of the sentiment analysis techniques. International Journal of Emajngineering and Advanced Technology (IJEAT), 9(1), 2009-2016.
Vidya, N. A., Fanany, M. I. ve Budi, I. (2015). Twitter sentiment to analyze net brand reputation of mobile phone providers. Procedia Computer Science, 72, 519-526.
Vinodhini, G. ve Chandrasekaran, R. M. (2014). Measuring the quality of hybrid opinion mining model for e-commerce application. Measurement, 55, 101-109.
Vohra, S. M. ve Teraiya, J. B. (2013). A comparatıve study of sentiment analysis techniques. Journal of Information. Knowledge And Research in Computer Engineering, 2(2), 313-317.
Wang, L., Wang, M., Guo, X., ve Qin, X. (2016). Microblog sentiment orientation detection using user interactive relationship. Journal of Electrical and Computer Engineering, 1-6.
Wang, Y., Kim, K., Lee, B. ve Youn, H. Y. (2018). Word clustering based on POS feature for efcient twitter sentiment analysis. Human-Centric Computing Information Science, 8(17), 1-25.
Wankhade, M., Rao, A. C. S. ve Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artifcial Intelligence Review, 55, 5731-5780.
Wassan, S., Chen, X., Shen, T., Waqar, M. ve Zaman, N. (2021). Amazon product sentiment analysis using machine learning techniques. Revista Argentina de Clínica Psicológica, 30, 695-703.
Xu, G., Meng, Y., Qiu, X., Yu, Z. ve Wu, X. (2019). Sentiment analysis of comment texts based on BiLSTM. IEEE Access (7), 51522-51532.
Yadav, A. ve Vishwakarma, D. K. (2020). Sentiment analysis using deep learning architectures: A review. Artificial Intelligence Review, 53, 4335–4385.
Yan, X. ve Huang, T. (2015). Tibetan sentence sentiment analysis based on the maximum entropy model. 10th International Conference on Broadband and Wireless Computing, Communication and Applications, 04-06 Kasım, Krakow, Polonya, 594-597.
Yi, J., Nasukawa, T., Bunescu, R. ve Niblack, W. (2003). Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. Third IEEE International Conference on Data Mining (ICDM’03), 22-22 Kasım, Melbourne, FL, ABD.
Zhang, L., Wang, S. ve Liu, B. (2017). Deep learning for sentiment analysis: A survey. WIREs Data Mining and Knowledge Discovery, 8(8), 1-25.
Zhao, J., Dong, L., Wu, J. ve Xu, K. (2012). MoodLens: An emoticon-based sentiment analysis system for Chinese tweets. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 12–16, Ağustos, Beijing, Çin, 1528-1531.
Zhao, Y., Dong, S. ve Li, L. (2014). Sentiment analysis on news comments based on supervised learning method. International Journal of Multimedia and Ubiquitous Engineering, 9(7), 333-346.
Zhu, J., Xu, C. ve Wang, H.-S. (2010). Sentiment classification using the theory of ANNs. The Journal of China Universities of Posts and Telecommunications, (17)1, 58-62.
Ziora, L. (2016). The sentiment analysis as a tool of business analytics in contemporary organizations. Studia Ekonomiczne, (281), 234-241.
Referanslar
Abbasi, A., Chen, H. ve Salem, A. (2008). Sentiment analysis in multiple languages. ACM Transactions on Information Systems, 26(3), 1-34.
Agarwal, B., Mittal, N., Bansal, P. ve Garg., S. (2015). Sentiment analysis using common-sense and context information. Hindawi Publishing Corporation Computational Intelligence and Neuroscience, 2015(6), 1-9.
Ahmed, A. A. A., Agarwal, S., Kurniawan, I. G. A., Anantadjaya, S. P. D. ve Krishman, C. (2022). Business boosting through sentiment analysis using Artificial Intelligence approach. International Journal of System Assurance Engineering and Management, 13, 699-709.
Ain, A. T., Ali, M., Riaz, A., Noureen, A., Kamran, M., Hayat, B. ve Rehman, A. (2017). Sentiment analysis using deep learning techniques: A review. International Journal of Advanced Computer Science and Applications, 8(6), 424-433.
Alaei, A. R., Becken, S. ve Stantic, B. (2017). Sentiment analysis in tourism: Capitalizing on big data. Journal of Travel Research, 1-7.
Alamoudi, E. S. ve Alghamdi, N. S. (2020). Sentiment classification and aspect-based sentiment analysis on yelp reviews using deep learning and word embeddings. Journal of Decision Systems, 1-23.
Al-Azzam, N. ve Shatnawi, I. (2021). Comparing supervised and semi-supervised machine learning models on diagnosing breast cancer. Annals of Medicine and Surgery, 62, 53-64.
Alhumoud, S. O., ve Al Wazrah, A. A. (2021). Arabic sentiment analysis using recurrent neural networks: A review. Artificial Intelligence Review. DOI:10.1007/s10462-021-09989-9.
Alzamzami, F., Hoda, M. ve El Saddık, A. (2020). Light gradient boosting machine for general sentiment classification on short texts: A comparative evaluation. IEEE Access, 8, 101840-101858.
Appel, O., Chiclana, F., Carter, J. ve Fujita, H. (2016). A hybrid approach to the sentiment analysis problem at the sentence level, Knowledge-Based Systems, 108, 110-124.
Arora, D., Li, K. F. ve Neville, S. W. (2015). Consumers’ sentiment analysis of popular phone brands and operating system preference using Twitter data: A feasibility study. IEEE 29th International Conference on Advanced Information Networking and Applications, 24-27 Mart, Gwangiu, Güney Kore.
Atan, S. ve Çınar, Y. (2019). Borsa İstanbul’da finansal haberler ile piyasa değeri ilişkisinin metin madenciliği ve duygu (sentiment) analizi ile incelenmesi. Ankara Üniversitesi SBF Dergisi, 74(1), 1-34.
Bahrainian, S.-A. ve Dengel, A. (2013). Sentiment analysis using sentiment features. 2013 IEEE/WIC/ACM International Conferences on Web Intelligence (WI) and Intelligent Agent Technology (IAT), 17-20 Kasım, Atlanta, GA, ABD, 26-29.
Balahur, A., Mihalcea, E. ve Montoyo, A. (2014). Computational approaches to subjectivity and sentiment analysis: Present and envisaged methods and applications. Computer Speech ve Language, 28(1), 1-6.
Balaji, T. K. ve Annavarapu, C. S. R. ve Bablani (2021). Machine learning algorithms for social media analysis: A survey. Computer Science Review, 40, 100395, 1-32.
Bartusiak, R., Augustyniak, L., Kajdanowicz, T. ve Kazienko, P. (2015). Sentiment analysis for polish using transfer learning approach. Second European Network Intelligence Conference, Eylül 21-22, Karlskrona, İsveç.
Behdenna, S., Barigou, F. ve Belalem, G. (2018). Document level sentiment analysis: A survey. EAI Endorsed Transactions on Context-aware Systems and Applications, 4(13), 1-8.
Bergsma, S., McNamee, P., Bagdouri, M., Fink, C. ve Wilson, T. (2012). Language identification for creating language-specific twitter collections. Proceedings of the 2012 Workshop on Language in Social Media (LSM 2012), Montreal, Kanada, Haziran 7, 65-74.
Bhadane, C., Dalal, H. ve Doshi, H. (2015). Sentiment analysis: Measuring opinions. Procedia Computer Science, 45, 808-814.
Birjali, M., Kasri, M. ve Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226:107134, 1-26.
Boiy, E. ve Moens, M-F. (2009). A machine learning approach to sentiment analysis in multilingual Web texts. Inf Retrieval, 12, 526-558.
Bose, R., Dey, R. K., Roy, S. ve Sarddar, D. D. (2020). Survey of twitter viewpoint on application of drugs by VADER sentiment analysis among distinct countries. Information and Communication Technology for Sustainable Development, 559-569.
Bouazizi, M. ve Ohtsuki, T. (2016). Sentiment analysis in Twitter: From classification to quantification of sentiments within tweets. IEEE Global Communications Conference (GLOBECOM), 04-08 Aralık, Washington, DC, ABD.
Cai, G. ve Xia, B. (2015). Convolutional Neural Networks for Multimedia Sentiment Analysis. Li, J., Ji, H., Zhao, D., Feng, Y. (Eds.). In Natural Language Processing and Chinese Computing. NLPCC 2015 2015. Lecture Notes in Computer Science, 9362. Springer, Cham.
Cambria, E. (2016). Affective computing and sentiment analysis. IEEE Intelligent Systems, 31(2), 102-107.
Caro, L. D. ve Grella, M. (2013). Sentiment analysis via dependency parsing. Computer Standards & Interfaces, 35, 442-453.
Chachra, A., Mehndiratta, P. ve Gupta, M. (2017). Sentiment analysis of text using deep convolution neural networks. Tenth International Conference on Contemporary Computing (IC3). 10-12 Ağustos, Noida, Hindistan.
Chaturvedi, S., Mishra, V. ve Mishra, N. (2017). Sentiment analysis using machine learning for business intelligence. IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), 21-22 Eylül, Chennai, Hindistan.
Chen, Y., Jiang, C., Wang, C.-Y., Gao, Y. ve Liu, J. K. R. (2015). Decision learning: Data analytic learning with strategic decision making. IEEE Signal Processing Magazine, 33(1), 34-56.
Cheng, Y., Yao, L., Xiang, G., Zhang, G., Thang, T. ve Zhong, L. (2020). Text sentiment orientation analysis based on multi-channel CNN and bidirectional GRU with attention mechanism. IEEE Access, 8, 134964- 134975.
Chitra, P., Karthik, T.S., Nithya, S., Jacinth Poornima, J., Srinivas Rao, J., Upadhyaya, M., Jayaram Kumar, K., Geethamani, R. ve Manjunath, T.C. (2020). Sentiment analysis of product feedback using natural language processing, 23, 1-4.
Ciocodeică, D.-F., Chivu, I.-C., Mihălcescu, H., Orzan, G., Băjan, A.-M. (2022). The degree of adoption of business ıntelligence in Romanian companies—The case of sentiment analysis as a marketing analytical tool. Sustainability, 14(7518), 1-20.
Collomb, A., Costea, C., D., Joyeux, Hasan, O. ve Brunie, L. (2013). A Study and comparison of sentiment analysis methods for reputation evaluation. Computer Science.
Cortes, C. ve Vapnik, V. (1995) Support-vector networks. Machine Learning, 20(3), 273-297.
Crawford, M., Khoshgoftaar, T. M., Prusa, J. D., Richter, A. N. ve Najada, H. A. (2015). Survey of review spam detection using machine learning techniques. Journal of Big Data, 2(23), 1-24.
D’Andrea, A., Ferri, F., Grifoni, P. ve Guzzo, T. (2015). Approaches, tools and applications for sentiment analysis implementation. International Journal of Computer Applications, 125(3), 26-33.
D’avanzo, E. ve Kuflik, T. (2013). E-commerce websites services versus buyers expectations: An empirıial analysis of the online marketplace. International Journal of Information Technology & Decision Making, 12(4), 651-677.
D’Avanzo, E. ve Pilato, G. (2014). Mining social network users opinions’ to aid buyers’ shopping decisions. Computers in Human Behavior, 1284-1295.
Desai, Z., Anklesaria, K. ve Balasubramaniam, H. (2021). Business Intelligence Visualization Using Deep Learning Based Sentiment Analysis on Amazon Review Data. IEEE 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 06-08 Temmuz, Kharagpur, Hindistan.
Devika, M. D., Sunitha, C. ve Ganesh, A. (2016). Sentiment analysis: A comparative study on different approaches. Procedia Computer Science, 87, 44-49.
Dey, L., Haque, M., Khurdiya, A. ve Shroff, G. (2017). Acquiring competitive ıntelligence from social media. Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data, 17 Eylül, Beijing, Çin.
Duwairi, R. M. ve Qarqaz, I. (2014). Arabic sentiment analysis using supervised classification. The 1st International Workshop on Social Networks Analysis, Management and Security (SNAMS - 2014), Ağustos 1-10, Barselona, İspanya.
Eisenstein, J. (2016). Unsupervised Learning for lexicon-based classification. arXiv:1611.06933.
Ficamos, P., Liu, Y. ve Chen, W. (2017). A Naive bayes and maximum entropy approach to sentiment analysis: Capturing domain-specific data in weibo. IEEE International Conference on Big Data and Smart Computing (BigComp), 13-16 Şubat, Jeju, Güney Kore, 336-339.
García-Pablos, A., Cuadros, M. ve Rigau, G. (2018). W2VLDA: Almost unsupervised system for aspect based sentiment analysis. Expert Systems With Applications, 91, 127-137.
Ghiassi, M., Skinner, J. ve Zimbra, D. (2013). Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with Applications, 40, 6266-6282.
Giatsoglou, M., Vozalis, M. G., Diamantaras, K., Vakali, Sarigiannidis, A. G. ve Chatzisavvas, K. C. (2017). Sentiment analysis leveraging emotions and word embeddings. Expert Systems With Applications, 69, 214-224.
Go, A., Bhayani, R. ve Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N project report, Stanford 1.
Gruzd, A., ve Haythornthwaite, C. (2013). Enabling community through social media. Journal of Medical Internet Research, 15(10), e248, 1-17.
Han, B. (2021). Comparison of different machine learning algorithms in classification. Journal of Physics: Conference Series, 2037, 1-5.
He, W., Shen, J., Tian, X., Li, Y., Akula, V., Yan, G. ve Tao, R. (2015). Gaining competitive intelligence from social media data. Industrial Management ve Data Systems, 115(9), 1622-1636.
He, W., Zha, S. ve Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33, 464-472.
Hemmatian, F. ve Sohrabi, M. K. (2019). A survey on classification techniques for opinion mining and sentiment analysis. Artificial Intelligence Review, 52(1), 1495-1545.
Hong, S., Lee, J. ve Lee, J-H. (2014). Competitive self-training technique for sentiment analysis in mass social media. Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS). 03-06 Aralık, Kitakyushu, Japonya, 9-12.
Hutto, C. J., ve Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings Of The 8th International Conference on Weblogs And Social Media, 8(1), 9-10.
Jain, A. ve Jain, M. (2017). Location based Twitter opinion mining using common-sense information. Global Journal of Enterprise Information System, 9(2), 28–32.
Jeyapriya, A. ve Selvi, K. (2015) Extracting aspects and mining opinions in product reviews using supervised learning algorithm. 2nd International conference on electronics and communication systems, 26-27 Şubat, Coimbatore, Hindistan. IEEE, 548–552.
Kechaou, Z., Ammar, M. B. ve Alimi, A. M. (2011). Improving e-learning with sentiment analysis of users’ opinions. Global Engineering Education Conference (EDUCON), 4- 6 Nisan, Ammani Jordan, 1032-1038.
Khan, A., Baharudin, B., Lee, L. H. ve Khan, K. (2010). A review of machine learning algorithms for text-documents classification. Journal Of Advances In Informatıon Technology, 1(1), 4-20.
Khan, S. (2022). Business intelligence aspect for emotions and sentiments analysis. First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), 16-18 Şubat, Trichy, Hindistan.
Kharde, V. A. ve Sonawane, S. S. (2016). Sentiment analysis of twitter data: A survey of techniques. International Journal of Computer Applications, 19(11), 5-15.
Kobayashi, N., Inui, K. ve Matsumoto, Y. (2007). Extracting aspect-evaluation and aspect-of relations in opinion mining. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 1065–1074, Haziran, Prag, Çek Cumhuriyeti, 1065-1074.
Kranjc, J., Smailović, J., Podpečan, V., Grčar, M., Žnidaršič, N. ve Lavrač, N. (2015). Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform. Information Processing and Management, 51, 187-203.
Kurnia, F. P. ve Suharjito. (2018). Business intelligence model to analyze social media information. Procedia Computer Science, 135, 5-14.
Lee, L. ve Pereira, F. (1999). Distributional similarity models: Clustering vs. nearest neighbors. Proceedings of the 37th Annual Meeting of the ACL, June, College Park, Maryland, ABD, 33-40.
Lee, H-j., Shin, H., Hwang, S-s., Cho, S. ve MacLachlan, D. (2010). Semi-supervised response modeling. Journal of Interactive Marketing, 24, 42–54.
Liang, B., Su, H., Gui, L., Camria, E. ve Xu, R. (2022). Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowledge-Based Systems, 235: 107643, 1-11.
Liu, B. (2011). Web data mining. Exploring hyperlinks, contents, and usage data. Springer: Chicago.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures On Human Language Technologies, 5(1), 1-167.
Madhoushi, Z., Hamdan, A. R. ve Zainudin, S. (2015). Sentiment analysis techniques in recent works. Science and Information Conference, Temmuz 28-30, Londra, İngiltere, 288-291.
Manek, A.S., Shenoy, P.D., Mohan, M.C. ve K.R., M. (2017). Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web, 20, 135–154.
Medhat, W., Hassan, A. ve Korashy, H. (2015). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113.
Micu, A., Micu, A. E., Geru, M. ve Lixandroiu, R. C. (2017). Analyzing user sentiment in social media: Implications for online marketing strategy. Psychology ve Marketing, 34(12), 1069-1134.
Muhammad, A., Wiratunga, N. ve Lothian, R. (2016). Contextual sentiment analysis for social media genres. Knowledge-Based Systems, 108, 92-101.
Nanli, Z., Ping., Z., Weiguo, L. ve Meng, C. (2012). Sentiment analysis: A literature review. International Symposium on Management of Technology (ISMOT), pp. 572–576
Nasreen Taj, M. B. ve Girisha, G. S. (2021). Insights of strength and weakness of evolving methodologies of sentiment analysis. Materials Today: Proceedings, 2, 157-162.
Neethu, M. S. ve Rajasree, R. (2013). Sentiment analysis in twitter using machine learning techniques. International Conference on Computing and Networking Technology (ICCNT), 04-06 Temmuz, Tiruchengode, Hindistan.
Pak, A. ve Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. Conference: Proceedings of the International Conference on Language Resources and Evaluation, LREC 2010, 17-23 Mayıs, Valletta, Malta, 1320-1326.
Pang, B. ve Lee, L. (2005). Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. Proceedings of the 43rd Annual Meeting of the ACL, Haziran, Ann Arbor, Michigan, 115-124.
Pang, B., ve Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Park, S. ve Kim, Y. (2016). Building thesaurus lexicon using dictionarybased approach for sentiment classification. IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA), 8-10 Haziran, Towson, MD, ABD.
Parveen, H. ve Pandey, S. (2016). Sentiment analysis on twitter data-set using naive bayes algorithm. 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 21-23 Temmuz, Karnataka, Hindistan.
Pathak, A. R., Pandey, M. ve Rautaray, S. (2021). Topic-level sentiment analysis of social media data using deep learning. Applied Soft Computing, 108, 1-17.
Peng, W. ve Park, D. H. (2011). Generate adjective sentiment dictionary for social media sentiment analysis using constrained nonnegative matrix factorization. Fifth International AAAI Conference on Weblogs and Social Media, 5(1), 273-180.
Pereira, D. A. (2020). A survey of sentiment analysis in the Portuguese language. Artificial Intelligence Review. DOI: 10.1007/s10462-020-09870-1
Poria, S., Cambria, E., Hazarika, D., Mazumder, N., Zadeh, A. ve Morency, L-P. (2017). Context-dependent sentiment analysis in user-generated videos. Proceedingsof the 55th Annual Meeting of the Association for Computational Linguistics, 30 Temmuz- 4 Ağustos, Vancouver, Kanada, 873-883.
Prakash, T. N. Ve Aloysius, A. (2020). Applications, approaches, and challenges in sentiment analysis (AACSA). International Research Journal of Modernization in Engineering Technology and Science, 2(7), 910-915.
Radhika, N. (2017). Music recommendation system based on user’s sentiment. Recent Innovations in Computational Intelligence and Image Processing, 23 Şubat, Kerala, Hindistan, 46-49.
Rahmath, H. P. ve Ahmed, T. (2014). Fuzzy based sentiment analysis of online product reviews using machine learning techniques. International Journal of Computer Applications, 99(17), 9-16.
Rokade, P. P. ve Kumari, A. (2019). Business intelligence analytics using sentiment analysis-a survey. International Journal of Electrical and Computer Engineering (IJECE), 9(1), 613-620.
Rosa, R. L., Rodríguez, D. Z. ve Bressan, G. (2015). Music recommendation system based on user’s sentiments extracted from social networks. IEEE Transactions on Consumer Electronics, 61(3), 359-367.
Saggion, H. ve Funk, A. (2009). Extracting opinions and facts for business intelligence. RNTI Journal, E (17), 119-146.
Saif, H., He, Y., Fernandez, M. ve Alani, H. (2016). Contextual semantics for sentiment analysis of Twitter. Information Processing and Management, 52, 5-19.
Šaloun, P., Hruzík, M. ve Zelinka, I. (2013). Sentiment analysis - e-bussines and e-learning common issue. 11th IEEE International Conference on Emerging eLearning Technologies and Applications, 24-25 Ekim, Stary Smokovec, The High Tatras, Slovakya, 339-343.
Salur, M. U. ve Aydın, İ. (2020). A novel hybrid deep learning model for sentiment classification. IEEE Access, (8), 58080-58093.
Samuels, A. ve Mcgonical, J. (2020). Sentiment analysis on social media content. arXiv:2007.02144.
Sánchez-Holgado, P., ve Arcila-Calderón, C. (2018). Towards the study of sentiment in the public opinion of science in Spanish. Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality - TEEM’18. Ekim,963–970, Salamanca, İspanya.
Saptal, M. S. ve Raut, R. (2021). Sentiment analysis in songs and advanced recommendation system. International Journal of Current Engineering and Technology, Özel Sayı 8.
Schuller, B., Mousa, A. E.-D., ve Vryniotis, V. (2015). Sentiment analysis and opinion mining: on optimal parameters and performances. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(5), 255–263.
Sharma, A. ve Ghose, U. (2020). Sentimental analysis of twitter data with respect to general elections in India. Procedia Computer Science, 173, 325-334.
Shivapvasad, T. K. ve Shetty, J. (2017). Sentiment analysis of product reviews: A review. International Conference on Inventive Communication and Computational Technologies (ICICCT 2017), 10-11 Mart, Coimbatore, Hindistan.
Smeureanu, I. ve Bucur, C. (2012). Applying supervised opinion mining techniques on online user reviews. Informatica Economică, 16(2), 81-91.
Sohrabi, M. K. ve Karimi, F. (2018). A feature selection approach to detect spam in the facebook social network. Arabian Journal for Science and Engineering, 43, 949-958.
Sreesurya, I., Rathi, H., Jain, P. ve Jain, T. K. (2020). Hypex: A tool for extracting business intelligence from sentiment analysis using enhanced LSTM. Multimedia Tools and Applications, 73, 35641-35663.
Stojanovski, D., Strezoski, G., Madjarov, G., Dimitrovski, I. ve Chorbev, I. (2018). Deep neural network architecture for sentiment analysis and emotion identification of Twitter messages. Multimedia Tools and Applications, 77, 32213-32242.
Sun, T., Wang, J., Zhang, P., Cao, Y., Liu, B. ve Wang, D. (2017). Predicting stock price returns using microblog sentiment for Chinese stock market. 3rd International Conference on Big Data Computing and Communications, 10-11 Ağustos, Chengdu, Çin.
Taherdoost, H. ve Madanchian, M. (2023). Artificial intelligence and sentiment analysis: A review in competitive research. Computers, 12(17), 1-15.
Tang, H., Tan, S. ve Cheng, X. (2009). A survey on sentiment detection of reviews. Expert Systems with Applications, 36, 10760-10773.
Teixeira, A. ve Laureano, R. M. S. (2017). Data extraction and preparation to perform a sentiment analysis using open source tools: The example of a Facebook fashion brand page. 12th Iberian Conference on Information Systems and Technologies (CISTI), 21-24 Haziran, Lizbon, Portekiz.
Thelwall, M., Wilkinson, D., ve Uppal, S. (2009). Data mining emotion in social network communication: Gender differences in MySpace. Journal of the American Society for Information Science and Technology, 61(1), 190–199. doi:10.1002/asi.21180
Thelwall, M., Buckley, K., ve Paltoglou, G. (2010). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406-418. doi:10.1002/asi.21462
Thet, T. T., Na, J.-C. ve Khoo, S. G. (2010). Aspect-based sentiment analysis of movie reviews on discussion boards. Journal of Information Science, 36(6), 823-848.
Torfi, A., Shirvani, R. A., Keneshloo, Y., Tavaf, N. ve Fox, E. A. (2020). Natural language processing advancements by deep learning: A survey. arXiv:2003.01200, 1-23.
Vafeiadis, T., Diamantaras, K., Sarigiannidis, G. ve Chatzisavvas, K. (2015), A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory, 55, 1-9.
Vashisht, G. ve Jailia, M. (2019). Panoptical view of the sentiment analysis techniques. International Journal of Emajngineering and Advanced Technology (IJEAT), 9(1), 2009-2016.
Vidya, N. A., Fanany, M. I. ve Budi, I. (2015). Twitter sentiment to analyze net brand reputation of mobile phone providers. Procedia Computer Science, 72, 519-526.
Vinodhini, G. ve Chandrasekaran, R. M. (2014). Measuring the quality of hybrid opinion mining model for e-commerce application. Measurement, 55, 101-109.
Vohra, S. M. ve Teraiya, J. B. (2013). A comparatıve study of sentiment analysis techniques. Journal of Information. Knowledge And Research in Computer Engineering, 2(2), 313-317.
Wang, L., Wang, M., Guo, X., ve Qin, X. (2016). Microblog sentiment orientation detection using user interactive relationship. Journal of Electrical and Computer Engineering, 1-6.
Wang, Y., Kim, K., Lee, B. ve Youn, H. Y. (2018). Word clustering based on POS feature for efcient twitter sentiment analysis. Human-Centric Computing Information Science, 8(17), 1-25.
Wankhade, M., Rao, A. C. S. ve Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artifcial Intelligence Review, 55, 5731-5780.
Wassan, S., Chen, X., Shen, T., Waqar, M. ve Zaman, N. (2021). Amazon product sentiment analysis using machine learning techniques. Revista Argentina de Clínica Psicológica, 30, 695-703.
Xu, G., Meng, Y., Qiu, X., Yu, Z. ve Wu, X. (2019). Sentiment analysis of comment texts based on BiLSTM. IEEE Access (7), 51522-51532.
Yadav, A. ve Vishwakarma, D. K. (2020). Sentiment analysis using deep learning architectures: A review. Artificial Intelligence Review, 53, 4335–4385.
Yan, X. ve Huang, T. (2015). Tibetan sentence sentiment analysis based on the maximum entropy model. 10th International Conference on Broadband and Wireless Computing, Communication and Applications, 04-06 Kasım, Krakow, Polonya, 594-597.
Yi, J., Nasukawa, T., Bunescu, R. ve Niblack, W. (2003). Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. Third IEEE International Conference on Data Mining (ICDM’03), 22-22 Kasım, Melbourne, FL, ABD.
Zhang, L., Wang, S. ve Liu, B. (2017). Deep learning for sentiment analysis: A survey. WIREs Data Mining and Knowledge Discovery, 8(8), 1-25.
Zhao, J., Dong, L., Wu, J. ve Xu, K. (2012). MoodLens: An emoticon-based sentiment analysis system for Chinese tweets. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 12–16, Ağustos, Beijing, Çin, 1528-1531.
Zhao, Y., Dong, S. ve Li, L. (2014). Sentiment analysis on news comments based on supervised learning method. International Journal of Multimedia and Ubiquitous Engineering, 9(7), 333-346.
Zhu, J., Xu, C. ve Wang, H.-S. (2010). Sentiment classification using the theory of ANNs. The Journal of China Universities of Posts and Telecommunications, (17)1, 58-62.
Ziora, L. (2016). The sentiment analysis as a tool of business analytics in contemporary organizations. Studia Ekonomiczne, (281), 234-241.