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A Proposal of Weight based Similarities Hybrid Algorithm on Social Media Posts through Crowdsourcing to Achieve High Performance Recommendation

Fayza Amreen, Md. Golam Muktadir, Tonmoy Hossain, Nazmus Sakib in Algorithms

International Journal of Applied Information Systems
Year of Publication: 2019
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Fayza Amreen, Md. Golam Muktadir, Tonmoy Hossain, Nazmus Sakib
10.5120/ijais2019451833
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  1. Fayza Amreen, Md. Golam Muktadir, Tonmoy Hossain and Nazmus Sakib. A Proposal of Weight based Similarities Hybrid Algorithm on Social Media Posts through Crowdsourcing to Achieve High Performance Recommendation. International Journal of Applied Information Systems 12(25):1-5, November 2019. URL, DOI BibTeX

    @article{10.5120/ijais2019451833,
    	author = "Fayza Amreen and Md. Golam Muktadir and Tonmoy Hossain and Nazmus Sakib",
    	title = "A Proposal of Weight based Similarities Hybrid Algorithm on Social Media Posts through Crowdsourcing to Achieve High Performance Recommendation",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "November, 2019",
    	volume = 12,
    	number = 25,
    	month = "November",
    	year = 2019,
    	issn = "2249-0868",
    	pages = "1-5",
    	url = "http://www.ijais.org/archives/volume12/number25/1068-2019451833",
    	doi = "10.5120/ijais2019451833",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

Recommendation based system on social media posts through crowd-sourcing is an ambitious task. This paper has formulated a hybrid algorithm acquaint with a new approach which is based on weight-based similarity to classify the social media posts as a positive or negative directional. In this paper, it has been proposed a scheme to use social media platforms taking crowd-source reactions and gathering information from the comments and posts by a user. The initial base post is generated by using the Raindrop algorithm where the credibility of the user account is factored as weight. The reaction from this base post can be used to determine whether the community is accepting the information of the base post or rejecting it with a negative impression. To find positively relevant comments and posts regarding the base post, the MinHash algorithm is used. Firstly, using substantial steps of Natural Language Processing (NLP) for pre-processing the data. Then the generalized MinHash algorithm is used to extract the relevant data from all the comments and the posts with the signature. Finally, Longest Common Subsequence (LCS) Algorithm is implemented to categorize the supporting most similar data, thus the post that triggered by the user will get the directional data from the relatively matched comments from the shingles.

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Keywords

Crowdsourcing, Data Mining, Natural Language Processing (NLP) , MinHash, Confusion Matrix, Raindrop, Longest Common Subsequence (LCS) Algorithm