CFP last date
22 April 2024
Reseach Article

Sentiment Analysis Approach based N-gram and KNN Classifier

by Akashdeep Dhiman, Dinesh Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 4
Year of Publication: 2018
Authors: Akashdeep Dhiman, Dinesh Kumar
10.5120/ijca2018917513

Akashdeep Dhiman, Dinesh Kumar . Sentiment Analysis Approach based N-gram and KNN Classifier. International Journal of Computer Applications. 182, 4 ( Jul 2018), 29-32. DOI=10.5120/ijca2018917513

@article{ 10.5120/ijca2018917513,
author = { Akashdeep Dhiman, Dinesh Kumar },
title = { Sentiment Analysis Approach based N-gram and KNN Classifier },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2018 },
volume = { 182 },
number = { 4 },
month = { Jul },
year = { 2018 },
issn = { 0975-8887 },
pages = { 29-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number4/29752-2018917513/ },
doi = { 10.5120/ijca2018917513 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:10:23.525029+05:30
%A Akashdeep Dhiman
%A Dinesh Kumar
%T Sentiment Analysis Approach based N-gram and KNN Classifier
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 4
%P 29-32
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The sentiment analysis is the approach which is design to analysis positive, negative and neural aspects towards any approach. In the past years, many techniques are designed for the sentiment analysis of twitter data. Based on the previous study about sentiment analysis, novel approach is presented in this research paper for the sentiment analysis of twitter data. The proposed approach is the combination of feature extraction and classification techniques. The N-gram algorithm is applied for the feature extraction and KNN classifier is applied to classify input data into positive, negative and neural classes. To validate the proposed system, performance is analyzed in terms of precision, recall and accuracy. The experiments results of proposed system show that it performs well as compared to existing system which is based on SVM classifier.

References
  1. A.A. Tzacheva and J. Ranganathan, “Action Rules for sentimental analysis using Twitter”, International Journal of Social Network Mining, 2017, in press.
  2. A. Bagavathi, A.A. Tzacheva, “Rule based Systems in Distributed Environment: Survey”, in Proceedings of International Conference on Cloud Computing and Applications (CCA17), 3rd World Congress on Electrical Engineering and Computer Systems and Science(EECSS’17),June 4-6 2017, Rome, Italy, pp 1-17
  3. Mohammad Rezwanul Huq, Ahmad Ali, Anika Rahman, “Sentiment Analysis on Twitter Data using KNN and SVM”, 2017, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 6, pp- 19-25
  4. Lopamudra Dey, Sanjay Chakraborty, Anuraag Biswas, Beepa Bose, Sweta Tiwari, “Sentiment Analysis of Review Datasets using Naïve Bayes’ and K-NN Classifier”, 2014, Research Paper publications.
  5. Payal B. Awachate, Prof. Vivek P. Kshirsagar, “Improved Twitter Sentiment Analysis Using NGram Feature Selection and Combinations”, 2016, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, Issue 9, pp- 154-157
  6. Yusuf Arslan, Aysenur Birturk, Bekjan Djumabaev, Dilek Kucuk, “Real-Time Lexicon-Based Sentiment Analysis Experiments On Twitter With A Mild (More Information, Less Data)Approach”, 2017 IEEE International Conference on Big Data (BIGDATA)
  7. Jaishree Ranganathan, Allen S. Irudayaraj, Angelina A. Tzacheva, “Action Rules for Sentiment Analysis on Twitter Datausing Spark”, 2017 IEEE International Conference on Data Mining Workshops
  8. Ankit Kumar Soni, “Multi-Lingual Sentiment Analysis of twitter data byusing classification algorithms”, 2017, IEEE
  9. Rashmi H Patil, Siddu P Algur, “Sentiment Analysis by Identifying the Speaker’s Polarity in Twitter Data”, 2017 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT)
  10. Heeyoung Lee, Angel Chang, Yves Peirsman, Nathanael Chambers, Mihai Surdeanu and Dan Jurafsky, “Deterministic coreference resolution based on entity-centric, precision ranked rules”, Computational Linguistics 39(4), 2013.
  11. Heeyoung Lee, Yves Peirsman, Angel Chang, Nathanael Chambers, Mihai Surdeanu, and Dan Jurafsky, “Stanfords Multi Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task”, In Proceedings of the CoNLL-2011 Shared Task, 2011.
  12. Karthik Raghunathan, Heeyoung Lee, Sudarshan Rangarajan, Nathanael Chambers, Mihai Surdeanu, Dan Jurafsky, Christopher Manning, “A Multi-Pass Sieve for Co reference Resolution EMNLP-2010”, Boston, USA, 2010.
  13. Marie-Catherine de Marneffe, Bill MacCartney and Christopher D. Manning, “Generating Typed Dependency Parses from Phrase Structure Parses”, 5th International Conference on Language Resources and Evaluation (LREC 2006).
  14. Marie-Catherine de Marneffe and Christopher D. Manning, “The Stanford typed dependencies representation”, COLING, Workshop on Cross-framework and Cross domain Parser Evaluation, 2008.
  15. Martín-Valdivia M T, Rushdi Saleh M, Urena-Lopez L A, Montejo-Raez A, “Experiments with SVM to classify opinions in different domains”, Expert Systems with Applications, 38(12), 14799- 14804,2011.
Index Terms

Computer Science
Information Sciences

Keywords

Sentiment analysis Classifier SVM KNN