期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：11
页码：689-696
出版社：Science and Information Society (SAI)
摘要：Due to widespread availability of Internet there are
a huge of sources that produce massive amounts of daily news.
Moreover, the need for information by users has been increasing
unprecedently, so it is critical that the news is automatically
classified to permit users to access the required news instantly
and effectively. One of the major problems with online news sets
is the categorization of the vast number news and articles. In
order to solve this problem, the machine learning model along
with the Natural Language Processing (NLP) is widely used for
automatic news classification to categorize topics of untracked
news and individual opinion based on the user’s prior interests.
However, the existing studies mostly rely on NLP but uses huge
documents to train the prediction model, thus it is hard to
classify a short text without using semantics. Few studies focus on
exploring classifying the news headlines using the semantics.
Therefore, this paper attempts to use semantics and ensemble
learning to improve the short text classification. The proposed
methodology starts with preprocessing stage then applying
feature engineering using word2vec with TF-IDF vectorizer.
Afterwards, the classification model was developed with different
classifier KNN, SVM, Naïve Bayes and Gradient boosting. The
experimental results verify that Multinomial Naïve Bayes shows
the best performance with an accuracy of 90.12% and recall
90%.
关键词：Natural language processing; feature engineering;
word embedding; text classification; ensemble learning
