@article{MAKHILLJEAS2017121314616,
    title = {Enhanced Feature for Short Document Classification},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {13},
    pages = {3534-3540},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.3534.3540},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.3534.3540},
    author = {Ali Abdulkadhim,Sabrina,Maryati Mohd,Umi and},
    keywords = {Short text,text classification,feature selection,ITC,WordNet,NB,J48,SVM},
    abstract = {Now a days, the use of short text has been increased dramatically in which many applications are
being relied on short text such as mobile messaging, breaking news social media and queries. The key
challenging behind the short text lies on the limitation of acquiring context information from such text. This
limitation increases both sparsity and ambiguity of the text. The traditional approaches that have been used
for the classical text such as bag-of-words, seems to be insufficient due to the too limited information that could
be extracted from the short text. This leads to loss the semantic knowledge and the semantic relations between
the words within the short text. Hence, this study aims to propose a new feature selection method based on
Interesting Term Count (ITC) with an external knowledge of WordNet and weighting to new weight (di) to
identify the variation between classes on the base of ITC. The proposed feature selection approach aims at
identifying the frequent terms without losing the semantic manner where the WordNet will be utilized in order
to provide the semantic correspondences among the words within the short text. Furthermore, three
classification methods have been used including support vector machine, J48 and Naive Bayes. The evaluation
has been performed by applying the three classifiers with the proposed feature selection method and without
the proposed feature selection method. Experimental results shown an outperformance of the classifiers with
the proposed feature selection method. This can imply the effectiveness behind using the proposed ITC with
external source knowledge for the short text classification.}
    }