Contextual affect analysis: a system for verification of emotion appropriateness supported with Contextual Valence Shifters

Abstract

This paper presents a novel method for estimating speaker's affective states based on two contextual features: valence shifters and appropriateness. Firstly, a system for affect analysis is used to recognise specific types of emotions. We improve the baseline system with the analysis of Contextual Valence Shifters (CVS), which determine the semantic orientation of emotive expressions. Secondly, a web mining technique is used to verify the appropriateness of the recognised emotions for the particular context. Verification of contextual appropriateness of emotions is the next step towards implementation of Emotional Intelligence Framework in machines. The proposed method is evaluated using two conversational agents.

ML-Ask: Open Source Affect Analysis Software for Textual Input in Japanese

“…Most commonly, the system was used to analyze user input in human-agent interaction [1,3,4,15,23,24,25,26]. In particular, the analysis of user input was utilized in decision making support about which conversation strategy to choose (normal conversation or joke) [1], and in an automatic evaluation method for dialog agents [3].…”

“…The system was most often evaluated on conversations, both between humans [1] and between human users and conversational agents [4,3,15,23,26]. In [1] Dybala et al showed that ML-Ask presents comparable answers to human annotators when annotating conversations between people of different age and status (in particular young students vs. middle-aged businessmen).…”

“…In other evaluations Ptaszynski et al showed that the system performs comparably to humans when annotating human-agent dialogs. This was evaluated using only ML-Ask [3,4], and ML-Ask confronted with the Webmining procedure [23,26]. Recently Ptaszynski et al added also emoticon analysis system CAO to this evaluation [35].…”

“…• Analysis of user input [1, 3, 4, 15] [23, 24, 25, 26] [35] • Decision making support [1,4] • Automatic evaluation [3] Verification of emotion appropriateness [23,24,25,26] [35]…”

Emotion Analysis and Dialogue Breakdown Detection in Dialogue of Chat Systems Based on Deep Neural Networks

“…The number of examples, the number of words, and the number of vocabularies are shown in Table 4. There are many approaches to estimating emotion from sentences using linguistic features [34][35][36][37][38]. These approaches often use machine learning methods such as neural networks for training the emotion estimators [39,40].…”