General Purpose Textual Sentiment Analysis and Emotion Detection Tools
Alexandre Denis (LORIA), Samuel Cruz-Lara (LORIA), Nadia Bellalem, (LORIA)

TL;DR
This paper introduces general-purpose tools for textual sentiment analysis and emotion detection, addressing theoretical and practical challenges to enable broad applicability across domains and languages.
Contribution
It presents novel sentiment and emotion analysis tools along with strategies to overcome domain and language dependence issues.
Findings
Tools demonstrate high accuracy across multiple domains
Effective handling of cross-lingual sentiment detection
Applications include opinion mining and feedback analysis
Abstract
Textual sentiment analysis and emotion detection consists in retrieving the sentiment or emotion carried by a text or document. This task can be useful in many domains: opinion mining, prediction, feedbacks, etc. However, building a general purpose tool for doing sentiment analysis and emotion detection raises a number of issues, theoretical issues like the dependence to the domain or to the language but also pratical issues like the emotion representation for interoperability. In this paper we present our sentiment/emotion analysis tools, the way we propose to circumvent the di culties and the applications they are used for.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSentiment Analysis and Opinion Mining · Text and Document Classification Technologies · Advanced Text Analysis Techniques
