Locally weighted linear regression for cross-lingual valence-arousal prediction of affective words

Dimensional sentiment analysis that aims to predict a continuous numerical value on multiple dimensions, such as the valence-arousal (VA) space, has attracted more attention in recent years. Compared to the categorical approach that focuses on sentiment classification such as binary classification (positive and negative), the dimensional approach can provide more fine-grained sentiment analysis. Therefore, recent studies have investigated the automatic development of affective lexicons with VA ratings because such resources are fundamental and useful for building dimensional sentiment applications. Due to the limited number of VA lexicons, a cross-lingual approach has emerged that aims to estimate the VA ratings of affective words of one language from those of another language based on linear regression or other regression methods. However, one of the major limitations of linear regression is the under-fitting problem which can cause a poor fit between the algorithm and the training data. To tackle this problem, this study proposes a locally weighted method to improve linear regression for predicting the valence-arousal values of affective words. This method performs a regression around the point of interest using only training data that are “local” to that point, and thus can reduce the impact of noise from unrelated training data. Experimental results show that the proposed method achieved a lower error rate and a higher correlation coefficient for predicting the VA ratings of Chinese affective words from English VA lexicons.