Sentiment Analysis of Twitter Feeds Using Flask Environment: A Superior Application of Data Analysis

Astha Modi, Khelan Shah, Shrey Shah, Samir Patel () and Manan Shah
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Astha Modi: Pandit Deendayal Energy University
Khelan Shah: Pandit Deendayal Energy University
Shrey Shah: Pandit Deendayal Energy University
Samir Patel: Pandit Deendayal Energy University
Manan Shah: Pandit Deendayal Energy University

Annals of Data Science, 2024, vol. 11, issue 1, No 7, 159-180

Abstract: Abstract In this challenging world, social media plays a vital role as it is at the pinnacle of data sharing. The advancement in technology has made a huge amount of information available for data analysis and it is on the hotlist nowadays. Opinions of the people are expressed and shared across various social media platforms like Twitter, Facebook, and Instagram. Twitter is a prodigious platform containing an ample amount of data and analyzing the data is of topmost priority. One of the most widely utilized approaches for classifying an individual’s emotions displayed in subjective data is sentiment analysis. Sentiment analysis is done using various algorithms of machine learning like Support Vector Machine, Naive Bayes, Long Short-Term Memory, Decision Tree Classifier, and many more, but this paper aims at the generalized way of performing Twitter sentiment analysis using flask environment. Flask environment provides various inbuilt functionalities to analyze the sentiments of text into three different categories: positive, negative, and neutral. Also, it makes API calls to the Twitter Developer account to fetch the Twitter data. After fetching and analyzing the data, the results get displayed on a webpage containing the percentage of positive, negative, and neutral tweets for a phrase in a pie chart. It displays the language analysis for the same phrase. Furthermore, the webpage calls attention to the tweets done on that phrase and reveals the details of the tweets. Considering the major industry runners of three different sectors namely Enterprises, Sports Apparel Industry, and Multimedia Industry, we have analyzed and compared sentiments of two different Multinational companies from each sector.

Keywords: Sentiments; Twitter; API; Flask; Python; Webpage; HTML (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s40745-022-00445-1

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