Estimation of Inter-Sentiment Correlations Employing Deep Neural Network Models

TL;DR

This paper develops deep neural network models to analyze inter-sentiment correlations across different types of news texts, revealing complex emotional interactions and the influence of text features on sentiment interpretation.

Contribution

It introduces two neural network models for sentiment calculation and explores their effectiveness across multiple datasets with diverse features, addressing the gap in inter-sentiment correlation analysis.

Findings

Different features influence sentiment interpretation in objective and subjective texts.

Anger and love sentiments show controversial interpretation patterns.

Journalistic writing may evoke love initially but lead to anger after publication.

Abstract

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.