Name
Abstract | ||
|---|---|---|
Topological and dynamic features of complex networks have proven to be suitable for capturing text characteristics in recent years, with various applications in natural language processing. In this article we show that texts with positive and negative opinions can be distinguished from each other when represented as complex networks. The distinction was possible by obtaining several metrics of the networks, including the in-degree, out-degree, shortest paths, clustering coefficient, betweenness and global efficiency. For visualization, the obtained multidimensional dataset was projected into a 2-dimensional space with the canonical variable analysis. The distinction was quantified using machine learning algorithms, which allowed an recall of 70% in the automatic discrimination for the negative opinions, even without attempts to optimize the pattern recognition process. |
Year | Venue | Keywords |
|---|---|---|
2010 | TextGraphs@ACL | multidimensional dataset,dynamic feature,natural language processing,canonical variable analysis,negative opinion,complex network feature,global efficiency,clustering coefficient,automatic discrimination,2-dimensional space,complex network |
Field | DocType | Citations |
Visualization,Computer science,Betweenness centrality,Complex network,Artificial intelligence,Clustering coefficient,Recall,Machine learning | Conference | 3 |
PageRank | References | Authors |
0.43 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diego R. Amancio | 1 | 352 | 29.53 |
| Renato Fabbri | 2 | 7 | 7.35 |
| Osvaldo N. Oliveira Jr. | 3 | 247 | 17.25 |
| Maria G. V. Nunes | 4 | 9 | 2.43 |
| Luciano da F. Costa | 5 | 357 | 34.63 |