A Deep Learning-Based Sentiment Analysis Approach for Online Product Ranking With Probabilistic Linguistic Term Sets

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The probabilities linguistic term set (PLTS) is an efficient tool to represent sentimental intensities hidden in unstructured text reviews that are useful for multicriteria online product ranking. Traditional machine learning-based sentiment analysis methods adopted in existing studies to obtain PLTSs often result in unsatisfying prediction accuracy and, thus, inevitably affect product ranking results. To overcome this limitation, in this study, we propose a deep learning-based sentiment analysis approach to produce PLTSs from online product reviews to rank online products. A natural language processing-based method is first applied to extract product features and corresponding feature texts from online reviews. Then, state-of-the-art deep learning-based models are implemented to conduct the sentiment classification for online product/feature review texts. To ensure classification accuracy, we propose an experimental matching mechanism to identify the level of sentiment tendency for all rating labels of a review dataset and then match each label with the most appropriate linguistic term. The experimental results reveal that our matching mechanism can benefit the training of a text classification model to identify sentiment tendencies from review texts with high prediction accuracy and with the help of the trained classification model, our approach can predict sentimental intensities of the extracted features' texts in the form of PLTSs with competitive accuracy. A case study of applying PLTSs output from our approach to an online product decision-making problem is also provided to validate the applicability of our approach.