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IJSTR >> Volume 8 - Issue 8, August 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Survey On Text Categorization Using Sentiment Analysis

[Full Text]



Chaitanya Bhagat, Dr. Deepak Mane



K-Nearest Neighbors, Machine Learning, Naïve Bayes, sentiment analysis, Twitter.



Twitter is a blog website online on internet which offers the platform to humans to experience and talk their perspectives about troubles, occurrences, merchandise and exclusive mind. Sentiment Analysis is an open-ended subject of research in the text mining area. Using several systems gaining knowledge of algorithms for evaluation of sentiments from exceptional tweets having a most of one hundred forty phrases consistent with a tweet and proposes a studies technique for improvisation of class. This survey paper tries to provide an entire evaluation of the modern replace in this discipline. The most important goal of this survey is to give a complete picture of ways Machine studying strategies are used in Sentiment Analysis to get better effects in short details. Also, we will have a look at basic emotion’s classification into ternary lessons i.e. Fantastic, negative, neutral the usage of exclusive device learning algorithm and type into their subclasses i.e. Love, happiness, fun, neutral, hate, sadness, and anger the use of equal system getting to know algorithms.



[1] M. Bouazizi and T. Ohtsuki, “A Pattern-Based Approach for Multi-class Sentiment Analysis in Twitter,” in Proc. IEEE ACCESS, pp. 20617-20639, 2017.
[2] S. M. Mohammad and S. Kiritchenko, “Using Hashtags to Capture Fine Emotion Categories from Tweets,” in Computational Intelligence, vol. 31, no. 2, 2015, pp. 301–326.
[3] B. Plank and D. Hovy, “Personality Traits on Twitter or How to Get 1,500 Personality Tests in a Week,” in Proc. of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 2015, pp. 92–98.
[4] Ankur Goel, Jyoti Gautam, Sitesh Kumar, “Real Time Sentiment Analysis of Tweets Using Naive Bayes,” in 2nd International Conference on Next Generation Computing Technologies, Dehradun, 2016.
[5] Aparna Garimella and Rada Mihalcea, “Zooming in on Gender Differences in Social Media,” in Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, 2016.
[6] D. Bamman and N. A. Smith, “Contextualized Sarcasm Detection on Twitter,” in Proc. of the 9th Int. AAAI Conf. on Web and Social Media Citeseer, 2015, pp. 574–577.
[7] P. Praveen, P. Sudheer and K.Sudheer Kumar, “Public Sentiment Analysis On Movie Reviews,” in Computer Science and Engineering department, 2018.
[8] Apoorv Agarwal, Boyi Xie,Ilia Vovsha, Owen Rambow, Rebecca Passonneau, “Sentiment Analysis of Twitter Data,” in Department of Computer Science Columbia University New York, 2016.
[9] M. Bouazizi and T. Ohtsuki, ‘‘Sentiment analysis in Twitter: From classification to quantification of sentiments within tweets,’’ in Proc. IEEE GLOBECOM, 2016, pp. 1–6.
[10] Paolo Ferragina, Francesco Piccinno, Roberto Santoro, “On Analyzing Hashtags in Twitter,” in Dipartiment of di Informatica University of Pisa Proceedings of the Ninth International AAAI Conference on Web and Social Media, 2016.
[11] Wei Gao and Fabrizio Sebastiani, “Tweet Sentiment: From Classification to Quantification,” in Qatar Computing Research Institute Hamad bin Khalifa University PO Box 5825, Doha, Qatar IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2015.
[12] U. R. Hadeghatta, “Sentiment Analysis of Hollywood movies on Twitter,” in Proc. IEEE/ACM ASONAM, 2013, pp. 1401-1404.
[13] Mohammad Rezwanual Huq, Ahmad Ali, Anika Rahman, “Sentiment Analysis on Twitter Data using KNN and SVM,” in Proc. IJACSA, pp 2381-2388, 2017.
[14] Raheesa Safrin, K.R. Sharmila, T. S. Shri, Subangi, E. A. Vimal, “Fraud Detection of Facebook business page based on sentiment analysis,” in IRJET, pp. 2381-2388.
[15] Penqfei Liu, Xipenq Qiu, Xuanjing Huanq, “Research on Short Text Classification Algorithm Based on Neural Network,” in 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, pp. 1726-1729, 2018.
[16] Radhi Desai, “Sentiment Analysis of Twitter Data: A Survey,” in International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2018, pp. 464-470.
[17] Zhao Jianqiang, Gui Xiaolin, Zhang Xuejun, “Deep Convolution Neural Networks for Twitter Sentiment Analysis,” in IEEE ACCESS, 2018, pp. 23253-23260.
[18] Bala Durga Dharmavarapu, Jayanag Bayana, “Sarcasm Detection in Twitter using Sentiment Analysis,” in International Journal of Recent Technology and Engineering, ISSN: 2277-3878, Volume-8, Issue-1S4, 2019, pp. 642-644.
[19] Daneena Deeksha Dsouza, Deepika, Divya P. Nayak, Elveera Jenisha Machado, Adesh N. D “Sentimental Analysis of Student Feedback using Machine Learning Techniques,” in International Journal of Recent Technology and Engineering, ISSN: 2277-3878, Volume-8, Issue-1S4, 2019, pp. 986-991.
[20] Siva Parvathi, V, Yjn Lakshmi, “A Survey on Sentiment Analysis,” in International Journal for Innovative Engineering & Management Research, Vol. 7, No. 12, 2018, pp. 289-296.