Sentiment analysis: An automatic contextual analysis and ensemble clustering approach and comparison

Abstract Product reviews are one of the most important resources to determine public sentiment. The existing literature on review sentiment analysis mostly utilizes supervised models, which usually suffer from domain-dependency and require expensive manual labelling effort to provide training data. This article addresses these issues by describing a completely automatic and unsupervised approach to sentiment analysis. The method consists of two phases, which are contextual analysis and unsupervised ensemble learning. In the implementation of both phases, a sentiment lexicon, SentiWordNet, is deployed. Using effective contextual procedures and modifying the base learning component (the k-means algorithm) results in developing a successful approach to sentiment analysis which can overcome the domain-dependency and the labelling cost problems. The results show that the proposed nonrandom initialization of k-means yields a significant improvement compared to other algorithms. In terms of accuracy and performance, the proposed method is effective compared to supervised and unsupervised approaches. We also introduce new sentiment analysis problems relating to Australian airlines and home builders which could be potential benchmark problems in the sentiment analysis field. Our experiments on datasets from different domains show that contextual analysis and the ensemble phases improve the clustering performance in term of accuracy, stability and generalizability.