期刊名称：Journal of King Saud University @?C Computer and Information Sciences
印刷版ISSN：1319-1578
出版年度：2021
卷号：33
期号：5
页码：508-517
DOI：10.1016/j.jksuci.2018.04.003
出版社：Elsevier
摘要：Morphological processing of Indian languages is one of the most escalating fields in the era of Natural Language Processing (NLP) since the last decade. The evaluation of Asian languages is a highly relevant field in the times of text mining and information retrieval. The morphological evaluation of a text can be employed for extraction and classification of knowledge. This paper amalgamates morphological evaluation and sentiment prediction of Punjabi language text. The textual data for Punjabi language is concerned with farmer suicide cases reported for Punjab state of India. The pre-processing phase of this study involves morphological evaluation and normalization of Punjabi words to their respective canonical forms. The next phase carries out training and testing of deep neural network model on refined Punjabi tokens obtained from the earlier phase. The proposed model classifies Punjabi tokens into four negatively oriented classes tailored for farmer suicide cases. The average accuracies of sentiment prediction obtained after 10-fold cross validation are 93.85%, 88.53%, 83.3%, and 95.45% for the four respective classes. The proposed framework yields satisfactory results on 275 Punjabi text documents with the overall accuracy of 90.29% for sentiment classification.
