Open Access
ITM Web Conf.
Volume 40, 2021
International Conference on Automation, Computing and Communication 2021 (ICACC-2021)
Article Number 03032
Number of page(s) 9
Section Computing
Published online 09 August 2021
  1. Wei Zhao, Ziyu Guan, Long Chen, Xiaofei He, Deng Cai, Beidou Wang, Quan Wang,Weakly-Supervised Deep Embedding for Product Review Sentiment Analysis, IEEE Transactions on Knowledge and Data Engineering, Vol. 30, No. 1, January 2018. [Google Scholar]
  2. T. K. Shivaprasad,Jyothi Shetty,”Sentiment analysis of product reviews: A review”, 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT),doi 10.1109/ICICCT.2017.7975207 [Google Scholar]
  3. Y. Fang, Hai Tan, Multi-Strategy Sentiment Analysis of Consumer Reviews Based on Semantic Fuzziness, INSPEC Accession Number: 17745421, DOI: 10.1109/ACCESS.2018.2820025, IEEE, Page(s): 20625–20631. [Google Scholar]
  4. W. Songpan, The Analysis and Prediction of Customer Review Rating Using Opinion Mining, 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA), NSPEC Accession Number: 17010009, 10.1109/SERA.2017.7965709, IEEE. [Google Scholar]
  5. R. Abinaya, P. Aishwaryaa, S. Baavana, N.D. Thamarai Selvi, Automatic Sentiment Analysis of User Reviews, 2016 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), NSPEC Accession Number: 16559951, 10.1109/TIAR.2016.7801231, IEEE [Google Scholar]
  6. M. Kavousi, Sepehr Saadatmand, Estimating the Rating of the Reviews Based on the Text, First International Conference on Data Analytics & Learning 2018. [Google Scholar]
  7. S. Zirpe; Bela Joglekar, Polarity Shift Detection Approaches in Sentiment Analysis: A survey, 2017 International Conference on Inventive Systems and Control (ICISC), INSPEC Accession Number: 17259793,DOI: 10.1109/ICISC.2017.8068737,IEEE [Google Scholar]
  8. M. Kulkarni, Mayuri Lingayat, Effective Product Ranking Method based on Opinion Mining, June 2015International Journal of Computer Applications 120(18):33–37, DOI:10.5120/21331-4306 [CrossRef] [Google Scholar]
  9. J. Fontana Rava; Gabriella Pasi; Marco Viviani, Feature Analysis for Fake Review Detection through Supervised Classification, 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA),INSPEC Accession No.17521817, 10.1109/DSAA.2017.51, IEEE [Google Scholar]
  10. M. Chauhan, Divakar Yadav, Sentimental Analysis of Product Based Reviews Using Machine Learning Approaches, Journal of Network Communications and Emerging Technologies (JNCET) Volume 5, Special Issue 2, December(2015). [Google Scholar]
  11. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa. Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12(Aug):2493–2537, 2011. [Google Scholar]
  12. K. Dave, S. Lawrence, and D. M. Pennock. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th international conference on World Wide Web, pages 519–528. ACM, 2003. [Google Scholar]
  13. M. S. Elli and Y.-F. Wang. Amazon reviews, business analytics with sentiment analysis. [Google Scholar]
  14. S. Hota and S. Pathak. Knn classifier-based approach for multi-class sentiment analysis of twitter data. In International Journal of Engineering Technology, pages 1372–1375. SPC, 2018. [Google Scholar]
  15. B. Liu and L. Zhang. A Survey of Opinion Mining and Sentiment Analysis, pages 415–463. Springer US, Boston, MA,2012. [Google Scholar]
  16. C. Rain. Sentiment analysis in amazon reviews using probabilistic machine learning.Swarthmore College, 2013. [Google Scholar]
  17. R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Ng, and C. Potts. Recursive deep models for semantic compositionality over a sentiment treebank, In Proceedings of the 2013 conference on empirical methods in natural language processing, pages 1631–1642, 2013. [Google Scholar]
  18. Bo Pang and Lillian Lee, Opinion Mining and Sentiment Analysis, Foundations and Trends in Information Retrieval, 2008. [CrossRef] [Google Scholar]
  19. Theo M.V. Janssen, Frege, contextuality and compositionality, Computer Science, University of Amsterdam, 2002. [Google Scholar]
  20. Xin Wang, Yuanchao Liu, Chengjie Sun, Baoxun Wang and Xiaolong Wang,Predicting Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory, Harbin Institute of Technology, Harbin, China, 2015. [Google Scholar]
  21. Ziqian Zeng, Wenxuan Zhou, Xin Liu, Yangqiu Song, A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification, Computer Science, Computation and Language, 10 April 2019, arXiv:1904.05055. [Google Scholar]

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