PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages
release_qwuzzpigbfatbhpr7i6jx255iu
by
Dimitrios Zaikis, Nikolaos Stylianou, Ioannis Vlahavas
Abstract
Media analysis (MA) is an evolving area of research in the field of text mining and an important research area for intelligent media analytics. The fundamental purpose of MA is to obtain valuable insights that help to improve many different areas of business, and ultimately customer experience, through the computational treatment of opinions, sentiments, and subjectivity on mostly highly subjective text types. These texts can come from social media, the internet, and news articles with clearly defined and unique targets. Additionally, MA-related fields include emotion, irony, and hate speech detection, which are usually tackled independently from one another without leveraging the contextual similarity between them, mainly attributed to the lack of annotated datasets. In this paper, we present a unified framework to the complete intelligent media analysis, where we propose a shared parameter layer architecture with a joint learning approach that takes advantage of each separate task for the classification of sentiments, emotions, irony, and hate speech in texts. The proposed approach was evaluated on Greek expert-annotated texts from social media posts, news articles, and internet articles such as blog posts and opinion pieces. The results show that this joint classification approach improves the classification effectiveness of each task in terms of the micro-averaged F1-score.
In application/xml+jats
format
Archived Files and Locations
application/pdf 376.4 kB
file_dms7f5ifljgevjitklpii4j53a
|
mdpi-res.com (publisher) web.archive.org (webarchive) |
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:
2076-3417
access all versions, variants, and formats of this works (eg, pre-prints)
Crossref Metadata (via API)
Worldcat
SHERPA/RoMEO (journal policies)
wikidata.org
CORE.ac.uk
Semantic Scholar
Google Scholar