Improving Document-Level Sentiment Classification Using Importance of Sentences

TL;DR

This paper introduces a neural network model that automatically assesses the importance of sentences within documents to improve sentiment classification accuracy across various review domains.

Contribution

It proposes a novel deep neural network model with gate mechanisms to determine sentence importance, enhancing document sentiment analysis performance.

Findings

Outperforms previous models on multiple review datasets

Sentence importance significantly impacts sentiment classification accuracy

Model effective across diverse review domains

Abstract

Previous researchers have considered sentiment analysis as a document classification task, in which input documents are classified into predefined sentiment classes. Although there are sentences in a document that support important evidences for sentiment analysis and sentences that do not, they have treated the document as a bag of sentences. In other words, they have not considered the importance of each sentence in the document. To effectively determine polarity of a document, each sentence in the document should be dealt with different degrees of importance. To address this problem, we propose a document-level sentence classification model based on deep neural networks, in which the importance degrees of sentences in documents are automatically determined through gate mechanisms. To verify our new sentiment analysis model, we conducted experiments using the sentiment datasets in the four different domains such as movie reviews, hotel reviews, restaurant reviews, and music reviews. In the experiments, the proposed model outperformed previous state-of-the-art models that do not consider importance differences of sentences in a document. The experimental results show that the importance of sentences should be considered in a document-level sentiment classification task.