Name
Title | ||
|---|---|---|
GTI at SemEval-2016 Task 4: Training a Naive Bayes Classifier using Features of an Unsupervised System. |
Abstract | ||
|---|---|---|
This paper presents the approach of the GTI Research Group to SemEval-2016 task 4 on Sentiment Analysis in Twitter, or more specifically, subtasks A (Message Polarity Classification), B (Tweet classification according to a two-point scale) and D (Tweet quantification according to a two-point scale). We followed a supervised approach based on the extraction of features by a dependency parsing-based approach using a sentiment lexicon and Natural Language Processing techniques. |
Year | Venue | Field |
|---|---|---|
2016 | SemEval@NAACL-HLT | SemEval,Naive Bayes classifier,Sentiment analysis,Computer science,Dependency grammar,Lexicon,Natural language processing,Artificial intelligence,Machine learning |
DocType | Citations | PageRank |
Conference | 3 | 0.36 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan Juncal-Martínez | 1 | 66 | 5.62 |
| tamara alvarezlopez | 2 | 53 | 4.09 |
| Milagros Fernández Gavilanes | 3 | 51 | 6.01 |
| Enrique Costa-Montenegro | 4 | 343 | 26.83 |
| Francisco J González Castaño | 5 | 86 | 12.36 |