Recommendation of Distance Education Colleges to Students based on Alumni Feedback
Harshini G N1, Gobi N2, A. Rathinavelu3
1Harshini G N, Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu, India.
2Gobi N, Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu, India.
3Dr.A.Rathinavelu, Department of Computer Science and Engineering, Dr. Mahalingam college of engineering and technology, Pollachi, Tamilnadu, India.
Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 2357-2363 | Volume-9 Issue-1, May 2020. | Retrieval Number: F9511038620/2020©BEIESP | DOI: 10.35940/ijrte.F9511.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sentiment Analysis (SA) systems are very common because most people trust it based on the opinions, emotions, attitudes and feelings shared by the users for decision making purposes about the product, service, news analytics etc. Sentiment analysis or opinion mining is used to automatically detect and classify sentiments into positive, negative or neutral opinion on product or service through certain algorithms. The expeditious growth of internet leads to the increase of reviews about product, services, movies, restaurants or vacation destinations and organizations. In order to increase or decrease the market value of the product, spammers may give the fake ratings. Sentiment Analysis system face great difficulties in deploying the algorithms to classify each review as either honest review, posted by the customers after using the products, or spam review, posted by the individual spammer or spammer groups. Another major challenge faced by the sentiment analysis system is that it lacks the accuracy of predicting implicit and explicit features present in the dataset is low, which is the major challenge in opinion mining system. The proposed system deals with text pre-processing which helps in improving the overall performance of the sentiment analysis systems and an effective system is developed to identify the fake reviews present in the dataset. Association Rule Mining along with K-Means clustering is used to achieve higher efficiency in classification of implicit and explicit features. Lexicon method is used for the classification of sentiments into positive and negative polarities. The advantage of proposed system is that, it can identify and remove the fake reviews in the dataset and extraction of both implicit and explicit feature can be identified through Lexicon based Method along with its polarities.
Keywords: Association Rule Mining, Fake Reviews, K-Means Clustering, Lexicon Method, Opinion Mining.
Scope of the Article: Clustering