Sentiment Detection Using Fish Optimization Genetic Algorithm

Sentiment Detection Using Fish Optimization Genetic Algorithm

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2020 by IJETT Journal
Volume-68 Issue-12
Year of Publication : 2020
Authors : Sukhlal Sangule, Dr. Sunil Phulre
DOI :  10.14445/22315381/IJETT-V68I12P223

Citation 

MLA Style: Sukhlal Sangule, Dr. Sunil Phulre. Sentiment Detection Using Fish Optimization Genetic Algorithm International Journal of Engineering Trends and Technology 68.12(2020):140-145. 

APA Style:Sukhlal Sangule, Dr. Sunil Phulre. Sentiment Detection Using Fish Optimization Genetic Algorithm  International Journal of Engineering Trends and Technology, 68(12), 140-145.

Abstract
Digital platforms are growing day by day, as people invest a large time in them. So human thoughts for any product, service, organization are easily available on this media platform. Analysis of user comments was done by text mining for understanding the status of any service or product. The sentiment of digital comments was extracted in the form of positive or negative class. This paper has proposed a Fish Optimization Genetic Algorithm for Sentiment Detection (FOGASD) in digital content. Collective Volitive and Feeding operator has increased the sentiment performance of work as well. Patterns are extracted from the input content, and as per the genetic algorithm, the output class was assigned to the patterns. The experiment was performed on a real amazon dataset having two sentiment classes. Results show that the proposed work has increased the evaluation parameter values as compared to another existing algorithm.

Reference
[1] P Reza Zafarani, Mohammad Ali Abbasi, Huan Liu, Social Media Mining – An Introduction, Cambridge University Press, Publisher Location, 2014.
[2] Kavanaugh, A. L., Fox, E. A., Sheetz, S. D., Yang, S.Li, L. T., Shoemaker, D. J., …Xie, L. Social media use by the government: from the routine to the critical. Government Information Quarterly, 29(4), 2012.
[3] Twitter Usage Statistics – Live Internet Stats. (n.d.). Retrieved Oct. 22, from http://www.internetlivesstats.com/twitter-statistics. 2018.
[4] Liu, B., Sentiment analysis, and opinion mining. Synthesis lectures on human language technologies, 2012.
[5] Dandan Jiang1, Xiangfeng Luo1, Junyu Xuan, And Zheng Xu .“Sentiment Computing for the News Event-Based. on the Social Media Big Data”. Digital Object Identifier 10.1109/ACCESS.2016.2607218 IEEE Acess, 2017.
[6] Priyanka Patil, Pratibha Yalagi, Sentiment Analysis Levels and Techniques: A Survey in International Journal of Innovations in Engineering and Technology, April 2016.
[7] Isidro Peñalver-Martinez, Francisco Garcia-Sanchez, Rafael Valencia-Garcia,” Feature-based opinion mining through ontologies,” Expert Systems with Applications-2014.
[8] V.K. Singh, R. Piryani, A. Uddin, P. Waila," Sentiment Analysis of Movie Reviews," conference on IEEE-2013.
[9] Basha, S. M., Zhenning, Y., Rajput, D. S., Iyengar, N., &Caytiles, D. R.. Weighted Fuzzy Rule-Based Sentiment Prediction Analysis on Reviews. International Journal of Grid and Distributed Computing, 10 (6), (2017) 41-54.
[10] Ryosuke Harakawa, Takahiro Ogawa, And Miki Haseyama. Extracting Hierarchical Structure Of Web Video Groups Based On Sentiment-Aware Signed Network Analysis., Sept. 19, 2017.Digital Object Identifier 10.1109/Access.2017.2741098.
[11] Ying Fang, Hai Tan, And Jun Zhang. Multi-Strategy Sentiment Analysis Of Consumer Reviews Based On Semantic Fuzziness. Ieee Access May 2, 2018.Digital Object Identifier 10.1109/Access.2018.2820025.
[12] Lei Wang, JianweiNiu, And Shui Yu. Sentidiff: Combining Textual Information and Sentiment Diffusion Patterns For Twitter Sentiment Analysis.”Journal Of Latex Class Files, 14(8), August 2018.
[13] Dr. Nitesh M Sureja, Dr. Sanjay P Patel , Solving A Combinatorial Optimization Problem Using Artificial Fish Swarm Algorithm, International Journal of Engineering Trends and Technology 68(5) (2020) 27-32.
[14] Prof. Durgesh Wadbude, A.P. Jayshree Boaddh, Rajeev Kumar, "A Survey on Various Techniques and Characteristics of Text Document Fetching." ijsret.com 4(I) , Mar-apr-2018
[15] Lubomir Stanchev. Semantic Document Clustering Using Information from WordNet and DBPedia. IEEE 12th International Conference on Semantic Computing (ICSC), 2018.
[16] DANDAN JIANG, XIANGFENG LUO, JUNYU XUAN, AND ZHENG XU. Sentiment Computing for the News Event Based on the Social Media Big Data. IEEE Access, Mar. 15, 2017.
[17] X. Li, Z. Shao, J. Qian, Anoptimizing method base on autonomous animates: fish- swarm algorithm, Systems Engineering Theory and Practice 22 (2002) 32–38
[18] UCI ML Repository_Sentiment Analysis Dataset. Accessed: Jun. 8, 2018. Available: http://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentences.
[19] Farkhund Iqbal, Jahanzeb Maqbool Hashmi, Benjamin C. M. Fung, Rabia Batool, Asad Masood Khattak, Saiqa Aleem, And Patrick C. K. Hung. A Hybrid Framework for Sentiment Analysis Using Genetic Algorithm Based Feature Reduction. IEEE Access Feb. 8, 2019.

Keywords
Classification, Sentiment analysis, Ontology, Text Mining, Un-directed Classification.