Name
Title | ||
|---|---|---|
FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings. |
Abstract | ||
|---|---|---|
This paper presents the approach developed at the Faculty of Engineering of University of Porto, to participate in SemEval 2017, Task 5: Fine-grained Sentiment Analysis on Financial Microblogs and News. The task consisted in predicting a real continuous variable from -1.0 to +1.0 representing the polarity and intensity of sentiment concerning companies/stocks mentioned in short texts. We modeled the task as a regression analysis problem and combined traditional techniques such as pre-processing short texts, bag-of-words representations and lexical-based features with enhanced financial specific bag-of-embeddings. We used an external collection of tweets and news headlines mentioning companies/stocks from Su0026P 500 to create financial word embeddings which are able to capture domain-specific syntactic and semantic similarities. The resulting approach obtained a cosine similarity score of 0.69 in sub-task 5.1 - Microblogs and 0.68 in sub-task 5.2 - News Headlines. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.18653/v1/s17-2155 | SemEval@ACL |
DocType | Volume | Citations |
Conference | abs/1704.05091 | 1 |
PageRank | References | Authors |
0.35 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro Saleiro | 1 | 19 | 5.63 |
| Eduarda Mendes Rodrigues | 2 | 350 | 21.40 |
| Carlos Soares | 3 | 95 | 18.18 |
| Eugénio Oliveira | 4 | 974 | 111.00 |