Automatic Human Emotion Classification in Web Document Using Fuzzy Inference System (FIS): Human Emotion Classification

Automatic Human Emotion Classification in Web Document Using Fuzzy Inference System (FIS): Human Emotion Classification

P Mohamed Shakeel (Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Malaysia) and S Baskar (Department of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, India)
Copyright: © 2020 |Pages: 11
DOI: 10.4018/IJTHI.2020010107

Abstract

Textual information mining deals with various information extraction methods that can be evolved from the rapid growth of textual information through human machine interface for analyzing emotions which are taken by a facial expression. The problem of emotions in text is concerned with the fast development of web 2.0 documents that are assigned by users with emotion labels, namely: sadness, surprise, happiness, empathy, anger, warmness, boredom, and amusement. Such emotions can give a new characteristic for document categorization. Textual information mining deals with various information extraction methods that can evolved from the rapid growth of textual information through a human machine interface for analyzing emotions, which are taken by a facial expression. The problem of emotions from text is concerned with the fast development of web 2.0 documents that are assigned by users with emotion labels. Such emotions can give a new characteristic for document categorization.
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Nowadays, people are very much aware with online communication and incline to express their feelings on the web. Considering this situation, the author Tim Li (2008) present a hybrid system based on efficiently mining emotional distress inclinations from publicly availableblogs.This blog is used to identify required people so as to give timely promote and intervention better public health. In this proposed system also describe a handcrafted model which includes human judgment and facilitates the adjustment of the forecast in machine learning on blog content.

To present the written text we acquire varied writing styles like informal and formal. Generally a small bit of text can express lots of emotional conditions,spirits or thoughts by means of linguistic and words.In order to extract the text emotions different techniques and approaches are utilized in the meadow of Opinion Mining and Sentiment Analysis.In paper Jasleen Kaur, Jatinderkumar Saini (2009) author analyzed the Formal and Informal text pieces in the meadow of Opinion Mining and Sentiment Analysis in universal languages. To analyze author considered 8 universal languages (English, Chinese, Arabic, Malaysian, Spanish, Turkish, Persian, Korean) formal and informal text from the poetry,poems,thesis and documents, etc., and also 4 feature selection parameters (IG, TF-IDF, n-gram, MI and MMI).The results showed that Arabian language has maximum performance and accuracy in the field of opinion Mining. From the experimented results between the IG and TF-IDF, parameters, IG performance is higher than all others.

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