The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Learning Algorithms of Sentiment Analysis: A Comparative Approach to Improve Data Goodness
Abstract
The uncritical application of automatic analysis techniques can be insidious. For this reason, the scientific community is very interested in the supervised approach. Can this be enough? This chapter aims to these issues by comparing three machine learning approaches to measuring the sentiment. The case study is the analysis of the sentiment expressed by the Italians on Twitter during the first post-lockdown day. To start the supervised model, it has been necessary to build a stratified sample of tweets by daily and classifying them manually. The model to be test provides for further analysis at the end of the process useful for comparing the three models: index will be built on the tweets processed with the aim of detecting the goodness of the results produced. The comparison of the three algorithms helps the authors to understand not only which is the best approach for the Italian language but tries to understand which strategy is to verify the quality of the data obtained.
Related Content
|
Tutita M. Casa, Fabiana Cardetti, Madelyn W. Colonnese.
© 2024.
14 pages.
|
|
R. Alex Smith, Madeline Day Price, Tessa L. Arsenault, Sarah R. Powell, Erin Smith, Michael Hebert.
© 2024.
19 pages.
|
|
Marta T. Magiera, Mohammad Al-younes.
© 2024.
27 pages.
|
|
Christopher Dennis Nazelli, S. Asli Özgün-Koca, Deborah Zopf.
© 2024.
31 pages.
|
|
Ethan P. Smith.
© 2024.
22 pages.
|
|
James P. Bywater, Sarah Lilly, Jennifer L. Chiu.
© 2024.
20 pages.
|
|
Ian Jones, Jodie Hunter.
© 2024.
20 pages.
|
|
|