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1(3), (2022):57-63. DOI: https://doi.org/10.46632/jbab/1/3/8
Moolpani Deepak Inder
A text is negative, positive, or neutral to identify that, the sentiment analysis engine Learning and Natural Language Processing uses. Two main approaches rule-based and automated Sentiment analysis. Logistic regression is a good model because it is even on large datasets Trains quickly and is very strong and provides results. Other good model choices include SVMs, Includes random forests, and naive bays. Sentiment analysis is sentiment mining Also referred to as, it is natural language (NLP) approach to processing, which is a Identify the emotional tone behind the text shows. About a product, service, or idea to determine and categorize concepts This is a popular way for companies. Feeling Analysis is performed Words as positive, negative, or neutral Text analysis and natural language for classification Using methods that use processing. It’s about companies branding their customers an overview of how they feel allows for getting. Sentiment analysis is an analytical technique which to determine the emotional meaning of communication Statistics, Natural Language Processing, and Machine Learning and uses learning. Company’s Customer messages, call center contacts, online Reviews, social media posts, and other content they use sentiment analysis to evaluate. Repustate’s sentiment analysis software can discover the sense of slang and emoji’s, and is the sentiment behind the message negative or Determine if positive. Restate your Try out the tool to see if it suits your needs offers a free trial. Sentiment analysis technique in GRA (Gray-related analysis) method Alternative: Accuracy, Precision, and Recall. Evaluation Preference: Random forest (RF), Support vector machine (SVM), K-nearest neighbor (KNN), Naïve Bayesian (NB). from the result it is seen that Random Forest (RF) and is got the first rank whereas is the K-nearest neighbor (KNN) got is having the lowest rank. The value of the dataset for Sentiment analysis technique in GRA (Gray-related analysis) method shows that it results in Random forest (RF) and top ranking.
[1]. Feldman, Ronen. “Techniques and applications for sentiment analysis.” Communications of the ACM56, no. 4 (2013): 82-89.
[2]. Madhoushi, Zohreh, Abdul Razak Hamdan, and Suhaila Zainudin. “Sentiment analysis techniques in recent works.” In 2015 science and information conference (SAI), pp. 288-291. IEEE, 2015.
[3]. Kaur, Harpreet, and Veenu Mangat. “A survey of sentiment analysis techniques.” In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 921-925. IEEE, 2017.
[4]. Annett, Michelle, and Grzegorz Kondrak. “A comparison of sentiment analysis techniques: Polarizing movie blogs.” In Conference of the Canadian Society for Computational Studies of Intelligence, pp. 25-35. Springer, Berlin, Heidelberg, 2008.
[5]. Medhat, Walaa, Ahmed Hassan, and Hoda Korashy. “Sentiment analysis algorithms and applications: A survey.” Ain Shams engineering journal5, no. 4 (2014): 1093-1113.
[6]. Hussein, Doaa Mohey El-Din Mohamed. “A survey on sentiment analysis challenges.” Journal of King Saud University-Engineering Sciences30, no. 4 (2018): 330-338.
[7]. Neethu, M. S., and R. Rajasree. “Sentiment analysis in twitter using machine learning techniques.” In 2013 fourth international conference on computing, communications and networking technologies (ICCCNT), pp. 1-5. IEEE, 2013.
[8]. Ghag, Kranti, and Ketan Shah. “Comparative analysis of the techniques for sentiment analysis.” In 2013 International Conference on Advances in Technology and Engineering (ICATE), pp. 1-7. IEEE, 2013.
[9]. Singh, Jaspreet, Gurvinder Singh, and Rajinder Singh. “A review of sentiment analysis techniques for opinionated web text.” CSI transactions on ICT4, no. 2 (2016): 241-247.
[10]. Jain, Anuja P., and Padma Dandannavar. “Application of machine learning techniques to sentiment analysis.” In 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 628-632. IEEE, 2016.
[11]. Dashtipour, Kia, Soujanya Poria, Amir Hussain, Erik Cambria, Ahmad YA Hawalah, Alexander Gelbukh, and Qiang Zhou. “Multilingual sentiment analysis: state of the art and independent comparison of techniques.” Cognitive computation8, no. 4 (2016): 757-771.
[12]. Tyagi, Priyanka, and R. C. Tripathi. “A review towards the sentiment analysis techniques for the analysis of twitter data.” In Proceedings of 2nd international conference on advanced computing and software engineering (ICACSE). 2019.
[13]. Bhuta, Sagar, Avit Doshi, Uehit Doshi, and Meera Narvekar. “A review of techniques for sentiment analysis of twitter data.” In 2014 International conference on issues and challenges in intelligent computing techniques (ICICT), pp. 583-591. IEEE, 2014.
[14]. Sharma, Anuj, and Shubhamoy Dey. “A comparative study of feature selection and machine learning techniques for sentiment analysis.” In Proceedings of the 2012 ACM research in applied computation symposium, pp. 1-7. 2012.
[15]. Araque, Oscar, Ignacio Corcuera-Platas, J. Fernando Sánchez-Rada, and Carlos A. Iglesias. “Enhancing deep learning sentiment analysis with ensemble techniques in social applications.” Expert Systems with Applications77 (2017): 236-246.
[16]. Ahmad, Shakeel, Muhammad Zubair Asghar, Fahad M. Alotaibi, and Irfanullah Awan. “Detection and classification of social media-based extremist affiliations using sentiment analysis techniques.” Human-centric Computing and Information Sciences9, no. 1 (2019): 1-23.
[17]. Coulter, Andre’ C. “Graybox software testing methodology: embedded software testing technique.” In Gateway to the New Millennium. 18th Digital Avionics Systems Conference. Proceedings (Cat. No. 99CH37033), vol. 2, pp. 10-A. IEEE, 1999.
[18]. DeMillo, Richard A., Dany S. Guindi, W. M. McCracken, A. Jefferson Offutt, and Kim N. King. “An extended overview of the Mothra software testing environment.” In Workshop on Software Testing, Verification, and Analysis, pp. 142-143. IEEE Computer Society, 1988.
[19]. Khan, Mohd. “Different approaches to black box testing technique for finding errors.” International Journal of Software Engineering & Applications (IJSEA)2, no. 4 (2011).
[20]. Lin, C. L. “Use of the Taguchi method and grey relational analysis to optimize turning operations with multiple performance characteristics.” Materials and manufacturing processes19, no. 2 (2004): 209-220.
[21]. Singh, P. Narender, K. Raghukandan, and B. C. Pai. “Optimization by Grey relational analysis of EDM parameters on machining Al–10% SiCP composites.” Journal of Materials Processing Technology155 (2004): 1658-1661.
[22]. Morán, J., E. Granada, J. L. Míguez, and J. Porteiro. “Use of grey relational analysis to assess and optimize small biomass boilers.” Fuel Processing Technology87, no. 2 (2006): 123-127.
[23]. Aslan, Nevzat, Alireza Aghajani Shahrivar, and Hadi Abdollahi. “Multi-objective optimization of some process parameters of a lab-scale thickener using grey relational analysis.” Separation and Purification Technology90 (2012): 189-195.
[24]. Chiang, Ko-Ta, and Fu-Ping Chang. “Optimization of the WEDM process of particle-reinforced material with multiple performance characteristics using grey relational analysis.” Journal of Materials Processing Technology180, no. 1-3 (2006): 96-101.
Moolpani Deepak Inder, “Sentiment Analysis Techniques in Recent Works Using GRA Methodology”, REST Journal on Banking, Accounting and Business, 1(3), (2022):57-63.