IDEAS home Printed from https://ideas.repec.org/r/eee/infome/v3y2009i2p143-157.html
   My bibliography  Save this item

Sentiment analysis: A combined approach

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Youngseok Choi & Habin Lee, 2017. "Data properties and the performance of sentiment classification for electronic commerce applications," Information Systems Frontiers, Springer, vol. 19(5), pages 993-1012, October.
  2. Yucel, Ahmet & Dag, Ali & Oztekin, Asil & Carpenter, Mark, 2022. "A novel text analytic methodology for classification of product and service reviews," Journal of Business Research, Elsevier, vol. 151(C), pages 287-297.
  3. Manosso, Franciele Cristina & Domareski Ruiz, Thays Cristina, 2021. "Using sentiment analysis in tourism research: A systematic, bibliometric, and integrative review," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 7, pages 16-27.
  4. Nan Jing & Tao Jiang & Juan Du & Vijayan Sugumaran, 2018. "Personalized recommendation based on customer preference mining and sentiment assessment from a Chinese e-commerce website," Electronic Commerce Research, Springer, vol. 18(1), pages 159-179, March.
  5. C. Sarai R. Avila, 2024. "Tweet Influence on Market Trends: Analyzing the Impact of Social Media Sentiment on Biotech Stocks," Papers 2402.03353, arXiv.org.
  6. Xiangfeng Luo & Yawen Yi, 2019. "Topic-Specific Emotion Mining Model for Online Comments," Future Internet, MDPI, vol. 11(3), pages 1-18, March.
  7. Mohammed Azmi Al-Betar & Ammar Kamal Abasi & Ghazi Al-Naymat & Kamran Arshad & Sharif Naser Makhadmeh, 2023. "Optimization of scientific publications clustering with ensemble approach for topic extraction," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2819-2877, May.
  8. Chen, Long-Sheng & Liu, Cheng-Hsiang & Chiu, Hui-Ju, 2011. "A neural network based approach for sentiment classification in the blogosphere," Journal of Informetrics, Elsevier, vol. 5(2), pages 313-322.
  9. F. Schweitzer & D. Garcia, 2010. "An agent-based model of collective emotions in online communities," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(4), pages 533-545, October.
  10. Shuyue Huang & Lena Jingen Liang & Hwansuk Chris Choi, 2022. "How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures," Sustainability, MDPI, vol. 14(5), pages 1-18, February.
  11. Yaxin Bi, 2022. "Sentiment classification in social media data by combining triplet belief functions," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(7), pages 968-991, July.
  12. Barış-Tüzemen Özge & Tüzemen Samet & Çelik Ali Kemal, 2023. "Sentiment analysis of reviews on cappadocia: The land of beautiful horses in the eyes of tourists," European Journal of Tourism, Hospitality and Recreation, Sciendo, vol. 13(2), pages 188-197, December.
  13. Abhijit Bera & Mrinal Kanti Ghose & Dibyendu Kumar Pal, 2021. "Sentiment Analysis of Multilingual Tweets Based on Natural Language Processing (NLP)," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(4), pages 1-12, October.
  14. Tran, Anh D. & Pallant, Jason I. & Johnson, Lester W., 2021. "Exploring the impact of chatbots on consumer sentiment and expectations in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
  15. Lima, Ana Carolina E.S. & de Castro, Leandro Nunes & Corchado, Juan M., 2015. "A polarity analysis framework for Twitter messages," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 756-767.
  16. Chmiel, Anna & Sobkowicz, Pawel & Sienkiewicz, Julian & Paltoglou, Georgios & Buckley, Kevan & Thelwall, Mike & Hołyst, Janusz A., 2011. "Negative emotions boost user activity at BBC forum," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(16), pages 2936-2944.
  17. Fan, Zhi-Ping & Che, Yu-Jie & Chen, Zhen-Yu, 2017. "Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis," Journal of Business Research, Elsevier, vol. 74(C), pages 90-100.
  18. Constantin Bratianu & Dan Florin Stanescu & Rares Mocanu & Ruxandra Bejinaru, 2021. "Serial Multiple Mediation of the Impact of Customer Knowledge Management on Sustainable Product Innovation by Innovative Work Behavior," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
  19. Shivendra Kumar & C. Ravindranath Chowdary, 2022. "Semantic model to extract tips from hotel reviews," Electronic Commerce Research, Springer, vol. 22(4), pages 1059-1077, December.
  20. David-Florin Ciocodeică & Raluca-Giorgiana (Popa) Chivu & Ionuț-Claudiu Popa & Horia Mihălcescu & Gheorghe Orzan & Ana-Maria (Dumitrache) Băjan, 2022. "The Degree of Adoption of Business Intelligence in Romanian Companies—The Case of Sentiment Analysis as a Marketing Analytical Tool," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
  21. Cristescu Marian Pompiliu & Nerişanu Raluca Andreea & Mara Dumitru Alexandru, 2022. "Using Data Mining in the Sentiment Analysis Process on the Financial Market," Journal of Social and Economic Statistics, Sciendo, vol. 11(1-2), pages 36-58, December.
  22. Damiano De Marchi & Rudy Becarelli & Leonardo Di Sarli, 2022. "Tourism Sustainability Index: Measuring Tourism Sustainability Based on the ETIS Toolkit, by Exploring Tourist Satisfaction via Sentiment Analysis," Sustainability, MDPI, vol. 14(13), pages 1-18, July.
  23. Tidor-Vlad Pricope, 2021. "Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review," Papers 2106.00123, arXiv.org.
  24. Hui Yuan & Wei Xu & Qian Li & Raymond Lau, 2018. "Topic sentiment mining for sales performance prediction in e-commerce," Annals of Operations Research, Springer, vol. 270(1), pages 553-576, November.
  25. Giacomo Manetti & Carmela Nitti & Marco Bellucci, 2022. "The accountability of Search and Rescue NGOs," Working Papers - Business wp2022_02.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
  26. A. Geethapriya & S. Valli, 2021. "An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment Analysis," Information Systems Frontiers, Springer, vol. 23(3), pages 791-805, June.
  27. Youngseok Choi & Habin Lee, 0. "Data properties and the performance of sentiment classification for electronic commerce applications," Information Systems Frontiers, Springer, vol. 0, pages 1-20.
  28. Mohammed Rushdi‐Saleh & M. Teresa Martín‐Valdivia & L. Alfonso Ureña‐López & José M. Perea‐Ortega, 2011. "OCA: Opinion corpus for Arabic," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(10), pages 2045-2054, October.
  29. Yong Shi & Luyao Zhu & Wei Li & Kun Guo & Yuanchun Zheng, 2019. "Survey on Classic and Latest Textual Sentiment Analysis Articles and Techniques," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1243-1287, July.
  30. Cristina Franciele & Thays Christina Domareski Ruiz, 2021. "Using sentiment analysis in tourism research: A systematic, bibliometric, and integrative review," Post-Print hal-03373984, HAL.
  31. Sandipan Sahu & Raghvendra Kumar & Pathan MohdShafi & Jana Shafi & SeongKi Kim & Muhammad Fazal Ijaz, 2022. "A Hybrid Recommendation System of Upcoming Movies Using Sentiment Analysis of YouTube Trailer Reviews," Mathematics, MDPI, vol. 10(9), pages 1-22, May.
  32. Gang Wang & Daqing Zheng & Shanlin Yang & Jian Ma, 2018. "FCE-SVM: a new cluster based ensemble method for opinion mining from social media," Information Systems and e-Business Management, Springer, vol. 16(4), pages 721-742, November.
  33. Pablo Sánchez-Núñez & Carlos de las Heras-Pedrosa & José Ignacio Peláez, 2020. "Opinion Mining and Sentiment Analysis in Marketing Communications: A Science Mapping Analysis in Web of Science (1998–2018)," Social Sciences, MDPI, vol. 9(3), pages 1-20, February.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.