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Products reviews are one of the major resources to determine the publicsentiment. The existing literature on reviews sentiment analysis mainlyutilizes supervised paradigm, which needs labeled data to be trained on andsuffers from domain-dependency. This article addresses these issues bydescribes a completely automatic approach for sentiment analysis based onunsupervised ensemble learning. The method consists of two phases. The firstphase is contextual analysis, which has five processes, namely (1) datapreparation; (2) spelling correction; (3) intensifier handling; (4) negationhandling and (5) contrast handling. The second phase comprises the unsupervisedlearning approach, which is an ensemble of clustering classifiers using amajority voting mechanism with different weight schemes. The base classifier ofthe ensemble method is a modified k-means algorithm. The base classifier ismodified by extracting initial centroids from the feature set via usingSentWordNet (SWN). We also introduce new sentiment analysis problems ofAustralian airlines and home builders which offer potential benchmark problemsin the sentiment analysis field. Our experiments on datasets from differentdomains show that contextual analysis and the ensemble phases improve theclustering performance in term of accuracy, stability and generalizationability.

An Automatic Contextual Analysis and Clustering Classifiers Ensemble approach to Sentiment Analysis