Evaluation of Unsupervised Anomaly Detection Methods in Sentiment Mining
K. Sudha1, N. Suguna2

1K. SUDHA, Research Scholar, Department of Computer and Information Sciences, Annamalai University.
2N. SUGUNA, Assistant Professor, Department of Computer Science and Engineering, Annamalai University.

Manuscript received on 31 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1080-1085 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8012078919/19©BEIESP | DOI: 10.35940/ijitee.I8012.078919

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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: Anomaly detection has vital role in data preprocessing and also in the mining of outstanding points for marketing, network sensors, fraud detection, intrusion detection, stock market analysis. Recent studies have been found to concentrate more on outlier detection for real time datasets. Anomaly detection study is at present focuses on the expansion of innovative machine learning methods and on enhancing the computation time. Sentiment mining is the process to discover how people feel about a particular topic. Though many anomaly detection techniques have been proposed, it is also notable that the research focus lacks a comparative performance evaluation in sentiment mining datasets. In this study, three popular unsupervised anomaly detection algorithms such as density based, statistical based and cluster based anomaly detection methods are evaluated on movie review sentiment mining dataset. This paper will set a base for anomaly detection methods in sentiment mining research. The results show that density based (LOF) anomaly detection method suits best for the movie review sentiment dataset.
Index Terms: Anomaly, Density, Distance, Cluster, Sentiment.

Scope of the Article: Performance Evaluation of Networks