WDE-FRAMEWORK FOR FINETUNNING PRODUCT REVIEW USING SENTIMENT ANALYSIS

Abstract

The reviews for a product is valuable for the upcoming buyers in helping them take decisions.
different opinion mining techniques have been proposed to judge a review sentence’s orientation (e.g.
positive or negative) is one of their key challenges. 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
supervision signals. The framework consists of two steps: (1) learning a high level representation (an
embedding space) which captures the general sentiment distribution of sentences through rating
information; (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-term memory. To evaluate the
proposed framework, we construct a dataset containing 1.1M weakly labeled review sentences and
11,754 labeled review sentences from Amazon. Experimental results show the efficacy of the
proposed framework and its superiority over baselines.