Historical Credibility for Movie Reviews and Its Application to Weakly Supervised Classification

TL;DR

This paper introduces a method called historical credibility to evaluate movie review trustworthiness using reviewer history, significantly improving classification efficiency and accuracy in weakly supervised models.

Contribution

It proposes a novel historical credibility measure based on reviewer history and applies it to enhance weakly supervised review classification.

Findings

Historical credibility effectively distinguishes trusted from distrusted reviews.

The proposed method significantly reduces annotation time and computational cost.

Classification accuracy improves with larger data sizes, outperforming text-only models.

Abstract

In this study, we deal with the problem of judging the credibility of movie reviews. The problem is challenging because even experts cannot clearly and efficiently judge the credibility of a movie review and the number of movie reviews is very large. To tackle this problem, we propose historical credibility that judges the credibility of reviews based on the historical ratings and textual reviews written by each reviewer. For this, we present three kinds of criteria that can clearly classify the reviews into trusted or distrusted ones. We validate the effectiveness of the proposed historical credibility through extensive analysis. Specifically, we show that characteristics between the trusted or distrusted reviews are quite distinguishable in terms of three viewpoints: 1) distribution, 2) statistics, and 3) correlation. Then, we apply historical credibility to a weakly supervised model to classify a given review as a trusted or distrusted one. First, we show that it is significantly efficient because the entire data set is annotated according to the predefined criteria. Indeed, it can annotate 6,400 movie reviews only in 0.093 seconds, which occupy only 0.55%~1.88% of the total learning time when we use LSTM and SVM as the learning model. Second, we show that the historical credibility-based classification model clearly outperforms the textual review-based classification model. Specifically, the classification accuracy of the former outperforms that of the latter by up to 11.7%~13.4%. In addition, we clearly confirm that our classification model shows higher accuracy as the data size increases.