Open AccessBusiness recommendation based on collaborative filtering and feature engineering – aproposed approach
Author(s) -
Prakash Rokade,
Aruna Kumari D
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i4.pp2614-2619
Subject(s) - sentiment analysis , computer science , sentence , polarity (international relations) , product (mathematics) , collaborative filtering , service (business) , feature (linguistics) , preprocessor , feature selection , natural language processing , analytics , artificial intelligence , feature engineering , recommender system , machine learning , data science , deep learning , linguistics , philosophy , genetics , geometry , mathematics , economy , cell , economics , biology
Business decisions for any service or product depend on sentiments by people. We get these sentiments or rating on social websites like twitter, kaggle. The mood of people towards any event, service and product are expressed in these sentiments or rating. The text of sentiment contains different linguistic features of sentence. A sentiment sentence also contains other features which are playing a vital role in deciding the polarity of sentiments. If features selection is proper one can extract better sentiments for decision making. A directed preprocessing will feed filtered input to any machine learning approach. Feature based collaborative filtering can be used for better sentiment analysis. Better use of parts of speech (POS) followed by guided preprocessing and evaluation will minimize error for sentiment polarity and hence the better recommendation to the user for business analytics can be attained.