DE-ForABSA: A Novel Approach to Forecast Automobiles Sales Using Aspect Based Sentiment Analysis and Differential Evolution

摘要

Today, amongst the various forms of online data, user reviews are very useful in understanding the user's attitude, emotion and sentiment towards a product. In this article, a novel method, named as DE-ForABSA is proposed to forecast automobiles sales based on aspect based sentiment analysis (ABSA) and ClusFuDE [8] (a hybrid forecasting model). DE-ForABSA consists of two phases – first, extracted user reviews of an automobile are analysed using ABSA. In ABSA, the reviews are pre-processed; aspects are extracted & aggregated to determine the polarity score of reviews. Second, uses of ClusFuDE consisting of clustering, fuzzy logical relationships and Differential Evolution (DE) to predict the sales of the automobile. DE is a population-based search method to optimize real values under the control of two operators: mutation & crossover. Score from phase 1 is a parameter in differential mutation in phase 2. The proposed method is tested on reviews & sales data of automobile. The empirical results show a Mean Square Error of 142.90 which indicates an effective consistency of the model.