Anomaly detection in sentiment mining refers to detecting user’s abnormal sentiment patterns in a large collection of sentiment data. The anomalies detected may be due to rapid sentiment changes that are hidden in a huge amount of text. The anomaly of sentiment data sources is a foremost factor in affecting the efficiency of sentiment classification methods. Thus, analyzing sentiment data to identify abnormal sentiment patterns in a timely manner is a valuable topic of research. In this work, it is analyzed how anomaly detection and elimination can aid sentiment classification and hence enhance sentiment mining. This paper proposes a model that combines the proposed anomaly detection method with meta-classification method to detect and eliminate anomalies and classify user’s sentiments. This paper also focuses on identifying the optimum percentage of data to be eliminated as anomalies after detection, so as to perform sentiment classification effectively on movie review data. The results exhibit the capabilities of the proposed method and offer better insight into this area of research.
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