Online reviews as a new textual domain offer a unique proposition for sentiment analysis. The reviewers usually give a whole rating score to the product. The potential customers tend to make decision according to the reviews. Previous works mainly focus on the summarization of the rating and sentiment of reviews. However, they ignore an important question. The whole rating can be regarded as linear regression of different aspect ratings. High aspect rating and low aspect rating compensate each other. Therefore, previous works are coarse-grained analysis. This paper first proposed a weak supervised learning method to extract implicit aspect with aspect seeds. It then formulates the aspect rating problem as a linear regression model. Finally a gradient descent method is proposed to handle the problem. Different datasets are collected. Experimental result in the datasets demonstrates the advantage of the proposed model.
Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem
