Researchers and readers of scientific articles face
the problem with identifying the articles and
scientific research papers categories and hence the
difficulty in determining authors' specialty. Many
researchers face the problem of selecting a journal
that is suitable for publishing his/her scientific
research paper. Many experiences assist
researchers in choosing the appropriate journal.
However, no one addresses the problem of
determining the publisher's specialty of the
scientific paper according to his / her article. This
paper proposes a solution to identify the author's
specialty through abstract comparison. Also, it
suggests a new method to help choose the
appropriate journal. That finds the appropriate
journal according to the abstract of the article that
is required to be published. A classification model
designs to find the correct category of a given
article. Accordingly, the author's specialty is
determined. The classifier also finds the Scimago
journal categories according to the journal's scope.
We built the classifier using a vector space model
based on a cosine similarity measure. Also, we use
M-TF-IDF weight which is a TF IDF, but we have
suggested a modified method that helps us with the
measurement. After classifying the article category,
a second classifier based on the Levenshtein
algorithm selects the appropriate journal for
publishing an article. Our dataset is divided into
three groups: the scopes of journals, the abstract of
articles, and the title of the journal and its scope
datasets—all datasets in the main category fromthe
Scimago website. The proposed measure shows
good performance of results. |
