Weakly Supervised Deep Embedding Product Review for Sentiment Analysis

Abstract

Product reviews are valuable for upcoming buyers in helping them make decisions. To this end, different opinion techniques have been proposed, where judging a review sentence's orientation (e.g. positive or negative) is one of their key challenges. Recently, deep learning has emerged as an effective means for solving sentiment classification problems. A neural network intrinsically learns a useful representation automatically without human efforts.

However, the success of deep learning highly relies on the availability of large-scale training data. We propose a novel deep learning framework for product review sentiment classification which employs prevalently available ratings as weak supervisionsignals.

The framework consists of two steps:

(1) learning a high-level representation (an embedding space) which captures the general sentiment distribution of sentences through ratinginformation;

(2) adding a classification layer on top of the embedding layer and use labeled sentences for supervised fine-tuning. We explore two kinds of low-level network structure for modeling review sentences, namely, convolution feature extractors and long short-termmemory