(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 12, 2017 213 | Page www.ijacsa.thesai.org Recommender System for Journal Articles using Opinion Mining and Semantics Anam Sardar, Javed Ferzund, Muhammad Asif Suryani, Muhammad Shoaib Department of Computer Science COMSATS Institute of Information Technology Sahiwal, Pakistan Abstract—Till date, the dominant part of Recommender Systems (RS) work focusing on single domain, i.e. for films, books and shopping and so on. However, human inclinations may traverse over numerous areas. Thus, utilization practices on related things from various domains can be valuable for RS to make recommendations. Academic articles, such as research papers are the way to express ideas and thoughts for the research community. However, there have been a lot of journals available which recognize these technical writings. In addition, journal selection procedure should consider user experience about the journals in order to recommend users most relevant journal. In this work of journal recommendation system, the data about the user experience targeting various aspects of journals has been gathered which addresses user experience about any journal. In addition, data set of archive articles has been developed considering the user experience in this regard. Moreover, the user experience and gathered data of archives are analyzed using two different frameworks based on semantics in order to have better consolidated recommendations. Before submission, we offer services on behalf of the research community that exploit user reviews and relevant data to suggest suitable journal according to the needs of the author. Keywords—Recommendation system; journal recommendation system; user opinion; sementic similarity; text analysis I. INTRODUCTION As the universe is getting digital, a large volume of structured, semi-structured and unstructured data is being generated very fast. This data is in terabytes, so it is referred as Big Data. Big data approaches are used to handle those types of datasets that are so big and complex that typically used applications software are not sufficient to exploit them fully. Because of the rapid increase in data volume, one is always flooded with Superfluity of choices in any domain [1]. A recommendation system uses the large volume of data in the form of text and sentiments available for summarization purpose to make serious and valid decisions. Recommender systems gather information from the users about their preferences for a particular item to make predictions for the product such as which bag I should buy or which paper I should read next [2]. Recommendations can be made based on user‟s interest which can be analyzed by the user‟s profile or considering their online or offline behavior e.g. RS is a subclass of information filtering system that tries to predict the “opinion” that a user would give to an item. Recommender frameworks have turned out to be amazingly common in recent years, and are used in an assortment of zones. Some prevalent applications incorporate music, books, movies, research papers; seek questions, social labels, and items in general. In any case, there are likewise recommended frameworks for specializations, partnership, jokes, eateries, life insurance, and Twitter pages [3]. Similarly, journal recommendation system has also become an important topic of discussion for research community which writes and publishes research articles, patents, and books. Because today we have numerous choices of journals that publish articles annually, quarterly, monthly and even bi-monthly, it becomes very difficult to choose an appropriate journal to submit your manuscript. With an increase in the publication of different research papers in multiple journals of diversified fields, authors find it difficult to choose an appropriate journal for their research work. In submission of journal, article may result in rejection and the main reason for rejection is that the paper is not submitted to a relevant journal even when the paper itself is excellent. So there is a need to develop a Recommendation system that can suggest suitable journals to the authors. The journal recommendation system can provide services to authors on behalf of publishers of academic journals. The choice of journal directly influences the authorial decisions like impact on practitioners, CV value of publication and acceptance or rejection risk [4]. Now the core problems that arise while building a journal recommendation system are:  which data set should be collected for a journal recommendation;  where to store this amount of big data of journals;  how to effectively perform data mining and sentiment analysis to make better journal recommendations;  Providing accurate recommendations to the users with accuracy and exactness;  which recommendation system technique would be best for journal recommendation system. The solution to the above-mentioned issues is our proposed solution that is based upon user opinion to make suitable journal recommendations. Existing systems for journal recommendation works by matching title and abstract of the papers [5] and do not consider the user experience with journals. Previously most of the work has been done by just content similarity and didn‟t focus on other aspects e.g. low- level features. Our proposed system not only considers the