TY - GEN
T1 - Reflective writing analysis approach based on semantic concepts
T2 - Intelligent Systems Conference, IntelliSys 2019
AU - Alrashidi, Huda
AU - Joy, Mike
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Automatic analysis of reflective writing involves identifying indicator strings and using string matching or rule matching processes, which flag sections of a text containing reflective material. The problem with the string-based approach is its inability to deal with knowledge inference from the text, such as the content, context, relevance, clarity, and interconnection, which can be identified by semantic analysis. The semantic analysis depends mainly on mapping the text into stored knowledge sources, such as WordNet, and analyzing the associations in the underlying knowledge source. In this paper, a semantic-based approach for reflective writing analysis is proposed, in which the input text, which is being analyzed, is mapped into semantic concepts. Moreover, a machine learning (ML) approach for reflective writing identification and analysis has been implemented to overcome the limitations of rule execution and keyword matching. The proposed approach addresses the efficiency of using several effective concepts, correlated with effective words that are identified in WordNet-Affect. The input text is classified into reflective or non-reflective categories, after which the input text is classified into various reflective classes, based on the type of the document. Moreover, the concepts in WordNet-Affect are evaluated and analyzed to demonstrate their effects on classification and labeling tasks.
AB - Automatic analysis of reflective writing involves identifying indicator strings and using string matching or rule matching processes, which flag sections of a text containing reflective material. The problem with the string-based approach is its inability to deal with knowledge inference from the text, such as the content, context, relevance, clarity, and interconnection, which can be identified by semantic analysis. The semantic analysis depends mainly on mapping the text into stored knowledge sources, such as WordNet, and analyzing the associations in the underlying knowledge source. In this paper, a semantic-based approach for reflective writing analysis is proposed, in which the input text, which is being analyzed, is mapped into semantic concepts. Moreover, a machine learning (ML) approach for reflective writing identification and analysis has been implemented to overcome the limitations of rule execution and keyword matching. The proposed approach addresses the efficiency of using several effective concepts, correlated with effective words that are identified in WordNet-Affect. The input text is classified into reflective or non-reflective categories, after which the input text is classified into various reflective classes, based on the type of the document. Moreover, the concepts in WordNet-Affect are evaluated and analyzed to demonstrate their effects on classification and labeling tasks.
KW - Automatic
KW - Classification
KW - Reflective
KW - Semantic-based
KW - WordNet-Affect
UR - https://www.scopus.com/pages/publications/85072844762
U2 - 10.1007/978-3-030-29513-4_23
DO - 10.1007/978-3-030-29513-4_23
M3 - Conference contribution
AN - SCOPUS:85072844762
SN - 9783030295127
T3 - Advances in Intelligent Systems and Computing
SP - 321
EP - 333
BT - Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2
A2 - Bi, Yaxin
A2 - Bhatia, Rahul
A2 - Kapoor, Supriya
PB - Springer Verlag
Y2 - 5 September 2019 through 6 September 2019
ER -