Identifying emotion by keystroke dynamics and text pattern analysis
Identifying emotion by keystroke dynamics and text pattern analysis
复制标题
DOI：
10.1080/0144929x.2014.907343
复制
发表时间：
2014-01-01
影响因子：
3.7
通讯作者：
Hasan, Kamrul
中科院分区：
文献类型：
--
作者：
Nahin, A. F. M. Nazmul Haque;Alam, Jawad Mohammad;Hasan, Kamrul
情感是一种认知过程，是人类区别于机器的重要特征之一。传统上，人类与计算机等机器之间的交互不会表现出任何情感交流。如果我们能够构建任何一个足够智能的系统，能够与涉及情感的人类进行交互，也就是说，它可以检测用户的情感并相应地改变其行为，那么使用机器可能会更有效和友好。已经采取了许多方法来检测用户情绪。情感计算是检测用户在特定时刻的情感的领域。本文中我们的方法是通过分析用户的键盘打字模式以及他们打字的文本（单词、句子）类型来检测用户情绪。这种组合分析给了我们一个有希望的结果，显示了从用户输入中检测到的大量情绪状态。采用多种机器学习算法对文本的时序属性和文本模式进行分析。我们之所以选择Android，是因为它是与计算机交互的最便宜和最可用的媒介。我们已经考虑了七种情绪类别来分类情绪状态。对于文本模式分析，我们使用向量空间模型和Jaccard相似度方法对自由文本输入进行分类。我们的综合方法在识别情绪方面的准确率超过80%。
Emotion is a cognitive process and is one of the important characteristics of human beings that makes them different from machines. Traditionally, interactions between humans and machines like computers do not exhibit any emotional exchanges. If we could build any system that is intelligent enough to interact with humans that involves emotions, that is, it can detect user emotions and change its behaviour accordingly, then using machines could be more effective and friendly. Many approaches have been taken to detect user emotions. Affective computing is the field that detects user emotion in a particular moment. Our approach in this paper is to detect user emotions by analysing the keyboard typing patterns of the user and the type of texts (words, sentences) typed by them. This combined analysis gives us a promising result showing a substantial number of emotional states detected from user input. Several machine learning algorithms were used to analyse keystroke timing attributes and text pattern. We have chosen keystroke because it is the cheapest and most available medium to interact with computers. We have considered seven emotional classes for classifying the emotional states. For text pattern analysis, we have used vector space model with Jaccard similarity method to classify free-text input. Our combined approach showed above 80% accuracies in identifying emotions.