Estimation of Inter-Sentiment Correlations Employing Deep Neural Network Models
It addresses sentiment correlation analysis for web mining, but appears incremental as it applies existing neural methods to a new aspect of sentiment analysis.
This paper tackles the problem of analyzing inter-sentiment correlations in web events by dividing social emotion into six categories and using deep neural network models for sentiment calculation on datasets of news titles, bodies, and comments, finding that anger and love are often misinterpreted between objective and subjective texts, reflecting sentiment complexity.
This paper focuses on sentiment mining and sentiment correlation analysis of web events. Although neural network models have contributed a lot to mining text information, little attention is paid to analysis of the inter-sentiment correlations. This paper fills the gap between sentiment calculation and inter-sentiment correlations. In this paper, the social emotion is divided into six categories: love, joy, anger, sadness, fear, and surprise. Two deep neural network models are presented for sentiment calculation. Three datasets - the titles, the bodies, the comments of news articles - are collected, covering both objective and subjective texts in varying lengths (long and short). From each dataset, three kinds of features are extracted: explicit expression, implicit expression, and alphabet characters. The performance of the two models are analyzed, with respect to each of the three kinds of the features. There is controversial phenomenon on the interpretation of anger (fn) and love (gd). In subjective text, other emotions are easily to be considered as anger. By contrast, in objective news bodies and titles, it is easy to regard text as caused love (gd). It means, journalist may want to arouse emotion love by writing news, but cause anger after the news is published. This result reflects the sentiment complexity and unpredictability.