Sentiment Analysis of U.S Airline Companies Twitter Data Using Hybrid Classifier

Clicks: 2
ID: 313090
2024
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #704 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Social media are global networks with various websites and applications that allow us to communicate, create and distribute great content with the rest of the world while communicating with the community at the same time. It is defined as computer-generated technologies that provide benefits such as the ability to express opinions, career-related professionals, and other forms of discourse through connected communities and networks around the world. This has a huge impact on society in many ways. Not only does it give us to touch and communication, but it also provides a lot of entertainment and catharsis and makes our lives easier in many ways. Machines must be able to categorize human emotions to enable an effective global connection. As a result, there is a very perfect sense of respect for effective collaboration between a human-machine, and social media also plays an essential role as they are a platform for ranking happy and unhappy reactions. Over the past few years, there seems to be a strong desire to teach machines to communicate like humans. Twitter, with over 330 million users, is a popular social network microblogging service that, like other social networks, allows you to send short messages and update your status, also known as tweets. We create a fast and efficient way to study customer feedback across the organization, bringing the business closer to market success. Many politicians, actors, and professionals use Twitter and actively seek meaningful goals such as activism, development, and mobilization. The stupendous number of responses to tweets makes it difficult to determine whether these tweets were positive or negative. This study proposes a new method to classify text based on emotional analysis: the Term Frequency - Inverse Document Frequency (TF-IDF), Bag of Words (BOG), and the Voting Classifier Support Vector Machine + Gradient Boosting (SVM + GB). The proposed strategy was tested on a Twitter Sentiment Analysis of U.S airline dataset and a modern machine model.
Reference Key
imported_1777058957_69ebc48dd3b02 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Abdul Waheed
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/488
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.