Human Abnormality Detection Based on Bengali Text

TL;DR

This paper introduces a novel Bengali text-based human abnormality detection model that analyzes typed text to classify normal or abnormal states, achieving up to 89% accuracy.

Contribution

First to develop a text-based human abnormality detection system using Bengali language with a new dataset and comparative classifier analysis.

Findings

Achieved 89% accuracy in abnormality detection.

Used Naive Bayes and SVM classifiers with TF-IDF and count vector features.

Created a Bengali dataset of 2000 sentences for this task.

Abstract

In the field of natural language processing and human-computer interaction, human attitudes and sentiments have attracted the researchers. However, in the field of human-computer interaction, human abnormality detection has not been investigated extensively and most works depend on image-based information. In natural language processing, effective meaning can potentially convey by all words. Each word may bring out difficult encounters because of their semantic connection with ideas or categories. In this paper, an efficient and effective human abnormality detection model is introduced, that only uses Bengali text. This proposed model can recognize whether the person is in a normal or abnormal state by analyzing their typed Bengali text. To the best of our knowledge, this is the first attempt in developing a text based human abnormality detection system. We have created our Bengali dataset (contains 2000 sentences) that is generated by voluntary conversations. We have performed the comparative analysis by using Naive Bayes and Support Vector Machine as classifiers. Two different feature extraction techniques count vector, and TF-IDF is used to experiment on our constructed dataset. We have achieved a maximum 89% accuracy and 92% F1-score with our constructed dataset in our experiment.