Hybrid Classification Technique for Sentiment Analysis of the Twitter Data
Jaanu Sharma1, Vinayak Khajuria2, Dilbag Singh3

1Jaanu Sharma, Department of CSE, Chandigarh university, India.
2Vinayak Khajuria, Assistant Prof. Department of CSE Chandigarh University, Gharuan, Mohali, Punjab, India.
3Dilbag Singh, Assistant Professor Department of CSE Chandigarh University, Gharuan, Mohali, Punjab, India

Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 1750-1753 | Volume-8 Issue-10, August 2019 | Retrieval Number: J91160881019/2019©BEIESP | DOI: 10.35940/ijitee.J9116.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Sentiment can be described in the form of any type of approach, thought or verdict which results because of the occurrence of certain emotions. This approach is also known as opinion extraction. In this approach, emotions of different peoples with respect to meticulous rudiments are investigated. For the attainment of opinion related data, social media platforms are the best origins. Twitter may be recognized as a social media platform which is socially accessible to numerous followers. When these followers post some message on twitter, then this is recognized as tweet. The sentiment of twitter data can be analyzed with the feature extraction and classification approach. The hybrid classification is designed in this work which is the combination of KNN and random forest. The KNN classifier extract features of the dataset and random forest will classify data. The approach of hybrid classification is applied in this research work for the sentiment analysis. The performance of the proposed model is tested in terms of accuracy and execution time.
Keywords: Sentiment analysis, KNN, SVM, random forest
Scope of the Article: Classification