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Implementation of Text Recommendation using Word Frequency and Cosine Similarity in Python

by Ahmad Farhan AlShammari
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 30
Year of Publication: 2023
Authors: Ahmad Farhan AlShammari
10.5120/ijca2023923065

Ahmad Farhan AlShammari . Implementation of Text Recommendation using Word Frequency and Cosine Similarity in Python. International Journal of Computer Applications. 185, 30 ( Aug 2023), 50-55. DOI=10.5120/ijca2023923065

@article{ 10.5120/ijca2023923065,
author = { Ahmad Farhan AlShammari },
title = { Implementation of Text Recommendation using Word Frequency and Cosine Similarity in Python },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2023 },
volume = { 185 },
number = { 30 },
month = { Aug },
year = { 2023 },
issn = { 0975-8887 },
pages = { 50-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number30/32888-2023923065/ },
doi = { 10.5120/ijca2023923065 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:27:31.568385+05:30
%A Ahmad Farhan AlShammari
%T Implementation of Text Recommendation using Word Frequency and Cosine Similarity in Python
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 30
%P 50-55
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The goal of this research is to develop a text recommendation program using word frequency and cosine similarity in Python. Text recommendation is the process that provides suggestions to the user. The word frequency is used to measure the importance of words in the text, and cosine similarity is used to measure the similarity between texts. The basic steps of text recommendation are explained: preprocessing text, creating list of words, creating bag of words, creating word frequency, calculating cosine similarity, creating similarity score, sorting similarity score, and printing recommendations. The developed program was tested on an experimental text from Wikipedia. The program successfully performed the basic steps of text recommendation and provided the required results.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Intelligence Machine Learning Natural Language Processing Text Mining Text Recommendation Word Frequency Cosine Similarity Python Programming.

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