ABSTRACT
Sentimental data is now a trend can be generally considered into two key types mainly facts and feelings. Facts are unbiased expressions around entities, actions, and their belongings. The thoughts of estimation in terms of sentiments are very extensive. In this paper, the main focus is given on the opinion terminologies that carry positive or negative thoughts. These thoughts are considered as sentiments. Plentiful work is done already using text processing in terms of mining of the information and recovery of the data. It is done using clustering approaches, mining of the text and other various text mining tasks but very less work is in handling of opinions in the medical field. Yet, sentiments are so imperative in the medical field to make decisions. The dataset on which the processing is done is the digital retinal DRIVE dataset was taken with 8-BPC (bits per color level) at 768 × 584 pixels. So this paper put light on the efficient approach for sentiment analysis using normalization and feature extraction for high classification rates and the simulation environment is used as MATLAB for development purpose.
- Nakov, Preslav, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, and Veselin Stoyanov. 2016. "SemEval- 2016 task 4: Sentiment analysis on Twitter." In Proceedings of the 10th international workshop on semantic evaluation (semeval-2016), pp. 1--18.Google Scholar
- Cambria, Erik. 2016 "Affective computing and sentiment analysis." IEEE Intelligent Systems 31, no. 2 (2016): 102--107. DOI: 10.1109/MIS.2016.31Google ScholarDigital Library
- Wang, Jin, Liang-Chih Yu, K. Robert Lai, and Xuejie Zhang. 2016. "Dimensional sentiment analysis using a regional CNN-LSTM model." In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 225--230. DOI: http://dx.doi.org/10.18653/v1/P16-2037Google Scholar
- Zhang, Meishan, Yue Zhang, and Duy-Tin Vo. 2016. "Gated neural networks for targeted sentiment analysis." In Thirtieth AAAI Conference on Artificial Intelligence. doi/10.5555/3016100.3016334Google Scholar
- Pagolu, Venkata Sasank, Kamal Nayan Reddy, Ganapati Panda, and Babita Majhi. 2016. "Sentiment analysis of Twitter data for predicting stock market movements." In 2016 international conference on signal processing, communication, power and embedded system (SCOPES), pp. 1345--1350. IEEE. DOI:10.1109/scopes.2016.7955659Google Scholar
- Appel, Orestes, Francisco Chiclana, Jenny Carter, and Hamido Fujita. 2016. "A hybrid approach to the sentiment analysis problem at the sentence level." Knowledge-Based Systems 108 (2016): 110--124 DOI: https://doi.org/10.1016/j.knosys.2016.05.040Google ScholarDigital Library
- Jindal, Stuti, and Sanjay Singh. 2015 "Image sentiment analysis using deep convolutional neural networks with domain specific fine tuning." In 2015 International Conference on Information Processing (ICIP), IEEE, pp. 447--451. https://doi.org/10.1109/INFOP.2015.7489424Google Scholar
- Islam, Jyoti, and Yanqing Zhang. 2016 "Visual sentiment analysis for social images using transfer learning approach." In 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable omputing and Communications (SustainCom)(BDCloud-SocialCom-SustainCom), IEEE, pp. 124--130. DOI: 10.1109/BDCloud-SocialCom-SustainCom.2016.29Google Scholar
- Anjaria, Malhar, and Ram Mohana Reddy Guddeti. 2014. "Influence factor based opinion mining of Twitter data using supervised learning." In 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS), IEEE, pp. 1--8.Google Scholar
- Wang, Yilin, Yuheng Hu, Subbarao Kambhampati, and Baoxin Li. 2015. "Inferring sentiment from web images with joint inference on visual and social cues: A regulated matrix factorization approach." In Ninth international AAAI conference on web and social media.Google Scholar
- Yu, Yuhai, Hongfei Lin, Jiana Meng, and Zhehuan Zhao. 2016. "Visual and textual sentiment analysis of a microblog using deep convolutional neural networks." Algorithms 9, no. 2 (2016): 41.DOI: 10.3390/a9020041Google Scholar
- You, Quanzeng, Jiebo Luo, Hailin Jin, and Jianchao Yang. 2015. "Robust image sentiment analysis using progressively trained and domain transferred deep networks." In Twenty-ninth AAAI conference on artificial intelligence. doi/10.5555/2887007.2887061Google Scholar
- Yang, Yang, Jia Jia, Shumei Zhang, Boya Wu, Qicong Chen, Juanzi Li, Chunxiao Xing, and Jie Tang. 2014. "How do your friends on social media disclose your emotions?." In Twenty-Eighth AAAI Conference on Artificial Intelligence.Google Scholar
- Frome, Andrea, Greg S. Corrado, Jon Shlens, Samy Bengio, Jeff Dean, and Tomas Mikolov. 2013 "Devise: A deep visual-semantic embedding model." In Advances in neural information processing systems, pp. 2121--2129.Google Scholar
- Xie, Xin, Songlin Ge, Fengping Hu, Mingye Xie, and Nan Jiang. 2019 "An improved algorithm for sentiment analysis based on maximum entropy." Soft Computing, 23, no. 2 (2019): 599--611. DOI:10.1007/s00500-017-2904-0Google ScholarDigital Library
Index Terms
- An Effectual Sentiment Analysis for High Classification Rates Using Medical Image Processing
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