Open AccessVote Recommendation System using Aspect based Machine Learning Approach
Author(s) -
Swati Sharma,
Monika Bansal
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f1311.089620
Subject(s) - government (linguistics) , task (project management) , field (mathematics) , computer science , work (physics) , empowerment , artificial intelligence , computer security , data science , political science , engineering , law , economics , management , mechanical engineering , philosophy , linguistics , mathematics , pure mathematics
Over time, the information on WWW has escalated exponentially, paramounting to embryonic research in the field of Data Analysis using Natural Language Processing (NLP) and Machine Learning (ML). As data is increasing day by day there is huge demand for data analysis to get subjective information and analyzing government data is very useful and demanding task. So, in this paper, an application is being developed which will recommend the user to which party to vote will be benignant for themselves and for country, depending on the area of interest of different users. The data is collected from various governmental websites of multiple areas like women empowerment, education, employment, child labor etc. which will enhance the authenticity of the output. The main ground of this research is to lubricate common people and politicians as well. For common people; is for deciding their precious vote, to which party to give will be good for themselves and nation too. For politicians; they will have an idea about themselves and other politicians that which party is preferable and which is not preferable in respective areas, so that the politicians can work accordingly.