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Research Article Open access CC BY 4.0

Sentiments Analysis Study for the Adaptation of Online Medical Forums

Zahriya Lawal Hassan, Abdulrashid Sani, Anas Tukur Balarabe

Asian Journal of Research in Computer Science · pp. 24–33 · Published 22 Mar 2023

10.9734/ajrcos/2023/v15i3322

Abstract

Online medical forums allow users to research medical treatments or conditions and gain support from other users dealing with similar issues. These forums have become increasingly popular over the past decade, helping connect medical patients and professionals from various backgrounds and creating a supportive online community. This paper evaluates the adaptation of online medical forums in Nigeria, to analyse the opinions of Nigerian citizens in using the medical system. In this research, a Tweepy API (python library/ module that contains the required object and functions for managing the Twitter data), textblob (python library for processing textual data), and matplotlib modules (for creating statistical charts) were used to extract related tweets from the Twitter. The project involves steps like creating a Twitter developer account, which gives the privilege to create a Twitter application and has keys for accessing online resources. The analysis begins by searching for the data, storing it, filtering it and then returning the sentiment analysis to review the positive, neutral, and negative tweets. The output of this project returns a table and scatter graph that displays the Subjectivity and polarity of the opinions of Nigerians on the adaptation of online medical forums. Similarly, a bar chart is obtained that shows the positive tweets, the negative tweets and the neutral regarding online medical forums.

Sentiment analysis online medical forums tweepy textblob matplotlib

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