Skip to main content
Pages and Files
CS department homepage
CS faculty meetings
CS talks and visitors
From Communities of Practice to Community Based Decision Making — Case Ericsson
Artificial General Intelligence and AI in Games
Cryptography Reading Group
Cybersecurity Breakfast Talks
Data Systems Group
DeIC offer Sep 2017
Former members of the department (partial list)
Jean Melo PhD
PhD Defense of Jean Melo
Remotely controlled drug delivery with chemical micro-robots.
State of Low-power Wireless Protocols for IoT
Talk Alan Mycroft 9 June 2017
Talk by Alexander Serebrenik on Aug 31, 2017
TALK by Rohit Gheyi on Aug 31, 2017
Add "All Pages"
Talk Carlos Gómez Rodríguez 19 May 2017
Rich linguistic processing for multilingual opinion mining
Carlos Gómez Rodríguez
University of A Coruña
Fri 19 May 2017 at 11:00-12:00
(part of ML Seminar Series)
Identifying the sentiment present in texts published on the Web and social networks like Twitter can provide an invaluable source of information about the public perception of products, issues or events.
In this talk, I will present an overview of our recent developments at the LyS research group in the field of opinion mining, using natural language processing and machine learning techniques to identify positive or negative sentiment on texts from the Web and social networks like Twitter. These include both rule-based and supervised approaches. The rule-based approach estimates the polarity of a text (i.e., whether its content expresses a positive or a negative opinion, or neither) based on its syntactic structure, using compositional syntax-based rules to account for relevant linguistic phenomena like intensification, adversative clauses and negation. The approach can be applied to multiple languages with minimal effort thanks to universal dependencies. The supervised approaches solve the same problem applying machine learning techniques, which can be enriched with syntactic and psychometric features or with feedback from the rule-based system.
help on how to format text
Turn off "Getting Started"