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About

About

I am part of REMINDS - RElevance MINing and Detection System project and my focus is on Sentiment Analysis on Social Networks.

I completed my Master's Degree in Computer Science in the Faculty of Science at the University of Porto

I graduated in Computer Science (Bsc) in the Faculty of Science at the University of Porto

Interest
Topics
Details

Details

  • Name

    Nuno Ricardo Guimarães
  • Cluster

    Computer Science
  • Role

    Research Assistant
  • Since

    01st December 2015
001
Publications

2017

Detecting Journalistic Relevance on Social Media: A two-case study using automatic surrogate features

Authors
Figueira, A; Guimarães, N;

Publication
Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, Sydney, Australia, July 31 - August 03, 2017

Abstract

2016

Lexicon Expansion System for Domain and Time Oriented Sentiment Analysis

Authors
Guimaraes, N; Torgo, L; Figueira, A;

Publication
KDIR: PROCEEDINGS OF THE 8TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL. 1

Abstract
In sentiment analysis the polarity of a text is often assessed recurring to sentiment lexicons, which usually consist of verbs and adjectives with an associated positive or negative value. However, in short informal texts like tweets or web comments, the absence of such words does not necessarily indicates that the text lacks opinion. Tweets like "First Paris, now Brussels... What can we do?" imply opinion in spite of not using words present in sentiment lexicons, but rather due to the general sentiment or public opinion associated with terms in a specific time and domain. In order to complement general sentiment dictionaries with those domain and time specific terms, we propose a novel system for lexicon expansion that automatically extracts the more relevant and up to date terms on several different domains and then assesses their sentiment through Twitter. Experimental results on our system show an 82% accuracy on extracting domain and time specific terms and 80% on correct polarity assessment. The achieved results provide evidence that our lexicon expansion system can extract and determined the sentiment of terms for domain and time specific corpora in a fully automatic form.