Aspect Term Extraction for Aspect Based Opinion Mining
M. Yesu Babu1, P. Vijaya Pal Reddy2, C. Shoba Bindu3

1Mr. M. Yesu Babu, Research Scholar, Department of CSE, JNTU Anantapur, Andra Pradesh, India.
2Dr. P. Vijaya Pal Reddy, Professor & Head, Department of CSE, Matrusri Engineering College, Hyderabad, Telangana, India.
3Dr. C. Shoba Bindu, Professor, Department of CSE, JNTUCE, Anantapur, Andhra Pradesh, India.

Manuscript received on 25 August 2019. | Revised Manuscript received on 03 September 2019. | Manuscript published on 30 September 2019. | PP: 2228-2233 | Volume-8 Issue-11, September 2019. | Retrieval Number: K20500981119/2019©BEIESP | DOI: 10.35940/ijitee.K2050.0981119
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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: Opinion Mining (OM) is also called as Sentiment Analysis (SA). Aspect Based Opinion Mining (ABOM) is also called as Aspect Based Sentiment Analysis (ABSA). In this paper, three new features are proposed to extract the aspect term for Aspect Based Sentiment Analysis (ABSA). The influence of the proposed features is evaluated on five classifiers namely Decision Tree (DT), Naive Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Conditional Random Fields (CRF). The proposed features are evaluated on the Two datasets on Restaurant and Laptop domains available in International Workshop on Semantic Evaluation 2014 i.e. SemEval 2014. The influence of proposed features is evaluated using Precision, Recall and F1 measures. The proposed features are highly influencing for aspect term extraction on classifiers. The performance of SVM and CRF classifiers with proposed features is more influencing for aspect term extraction compared with NB, DT and KNN classifiers.
Keywords: ABSA, KNN, Naïve Bayes, CRF, SVM, Decision Tree( DT).
Scope of the Article: