International Journal of Scientific & Engineering Research Volume 8, Issue 7, July-2017 2218 ISSN 2229-5518 IJSER © 2017 http://www.ijser.org Projecting the Future Direction of Publication PatternsUsing Text Mining Adebola K. Ojo Department of Computer Science, University of Ibadan, Nigeria adebola_ojo@yahoo.co.uk Abstract - In this study, text mining techniques were used to identify various research trends in academic journal publications. These techniques were applied to figure out trends in research patterns related to various specialisation areas in Computer Science academic journal articles within a period of two decades. The corpus mined were crawled online, pre-processed and transformed into structured data using filtering and stemming algorithms. The data were grouped into series of word features based on bag of words document representation. Abstracts and the keywords of the articles selected from these journal articles were used as the dataset. It was discovered that the publication trends have changed tremendously from communications and security to artificial intelligence over time. Index Terms– Computer Science, Filtering and Stemming Algorithms, Journals, Trends 1 INTRODUCTION Articles published in peer reviewed journals are likely to remain a very important means of distributing research findings for the foreseeable future [1].It is a mathematical technique that uses historical results to predict future outcome. During the publication of research articles in academic journals, it is necessary to identify trends. The process of identifying trends is called the trend analysis. Trend Analysis is a mathematical scientific approach that eliminates potential error by utilizing precise calculations in order to provide the utmost accuracy. It is the most dependable and efficient method for anticipating possible future behavior and desired outcome of a specific journal publication.It is a quantitative review of what happens over a period of time. Article abstracts provide a comprehensive yet concise overview of an article[2][3]. Abstracts are much shorter than the full text, which minimises the influence of data noise. Therefore, this study focused on prediction of abstracts of a journal article. The data used for this study were retrieved from the Institute of Electrical and Electronics Engineers (IEEE) Transactions on Computers, a monthly publication with a wide distribution to researchers, developers, technical managers, and educators in the computer field. It publishes papers on research in areas of current interest to the readers. Journal of Institute of Electrical and Electronics Engineers (IEEE) Transactions on Computers was chosen because it is one of the highly rated Computer Science journals with ISI indexed ranking and impact factors[4][5][6][7]. This study focused on the mining of trends of journal publications using the abstracts, keywords and authors’ bibliometric information of these journal articles. It was based on trend analysis using text mining techniques. Text mining is a multidisciplinary field, concerning retrieval of information, analysis of text, extraction of information, categorization, clustering, visualization, mining of data, and machine learning [8]. As the core of the knowledge management systems, text mining is a cross between information retrieval (IR) and artificial intelligence (AI). It is estimated that 90% of the world’s online content is based on text (Oracle Corporation). An effective means to deal with structured, numeric content has been developed via database management systems (DBMS), but text processing and analysis is significantly more difficult. The status of knowledge management systems is much like that of DBMS twenty years ago. The real challenges, and the potential payoffs for an effective, universal text solution, are equally appealing. Text mining predictive methods help organizations enhance the value of unstructured information by deploying insight from text analysis in software applications and business processes. Once textual information is transformed into a set IJSER