Name
Title | ||
|---|---|---|
A Latent Representation Model for Sentiment Analysis in Heterogeneous Social Networks. |
Abstract | ||
|---|---|---|
The growing availability of social media platforms, in particular microblogs such as Twitter, opened new way to people for expressing their opinions. Sentiment Analysis aims at inferring the polarity of these opinions, but most of the existing approaches are based only on text, disregarding information that comes from the relationships among users and posts. In this paper we consider microblogs as heterogeneous networks and we use an approach based on latent representation of nodes to infer, given a specific topic, the sentiment polarity of posts and users at the same time. The experimental investigation show that our approach, by taking into account both content and relationship information, outperforms supervised classifiers based only on textual content. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-15201-1_13 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Social network,Social media,Computer science,Sentiment analysis,Microblogging,Social network analysis,Natural language processing,Probabilistic latent semantic analysis,Artificial intelligence,Heterogeneous network | Conference | 8938 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.37 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Debora Nozza | 1 | 6 | 3.81 |
| Daniele Maccagnola | 2 | 32 | 3.82 |
| Vincent Guigue | 3 | 157 | 17.41 |
| Enza Messina | 4 | 214 | 23.18 |
| Patrick Gallinari | 5 | 1856 | 187.19 |