Polarity Identification of Aspect based Sentiment Reviews

Abstract

Objectives: To show the effectiveness of using random Fourier features in detecting sentiment polarities. Methods/ Statistical Analysis: The paper proposes on identifying the sentiment polarity of laptop and restaurant dataset towards three different polarity categories- positive, negative, and neutral. It provides experimental comparisons on conventional machine learning methods for detecting review polarities. Several articles have shown the effectiveness of random Fourier features are used in classification problems. The present paper prepares random Fourier features corresponding to the polarity data. A regularized least square strategy is adopted to fit a model and to perform the polarity detection task. Findings: Experiments were performed with 10 cross-validations. The proposed method with random Fourier features gives 80% accuracy over conventional classifiers. Initially the features are mapped to a lower dimension (chosen manually) and corresponding random Fourier features are obtained. The experiments are evaluated using Precision, recall, and F-measure. Application/Improvements: The method presented in this paper shows that aspect based polarity detection can be improved by choosing suitable features and mapping to lower dimension.