22,509 research outputs found
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
In this study, a novel Distributed Representation of News (DRNews) model is
developed and applied in deep learning-based stock market predictions. With the
merit of integrating contextual information and cross-documental knowledge, the
DRNews model creates news vectors that describe both the semantic information
and potential linkages among news events through an attributed news network.
Two stock market prediction tasks, namely the short-term stock movement
prediction and stock crises early warning, are implemented in the framework of
the attention-based Long Short Term-Memory (LSTM) network. It is suggested that
DRNews substantially enhances the results of both tasks comparing with five
baselines of news embedding models. Further, the attention mechanism suggests
that short-term stock trend and stock market crises both receive influences
from daily news with the former demonstrates more critical responses on the
information related to the stock market {\em per se}, whilst the latter draws
more concerns on the banking sector and economic policies.Comment: 25 page
A classification-based approach to economic event detection in Dutch news text
Breaking news on economic events such as stock splits or mergers and acquisitions has been shown to have a substantial impact on the financial markets. As it is important to be able to automatically identify events in news items accurately and in a timely manner, we present in this paper proof-of-concept experiments for a supervised machine learning approach to economic event detection in newswire text. For this purpose, we created a corpus of Dutch financial news articles in which 10 types of company-specific economic events were annotated. We trained classifiers using various lexical, syntactic and semantic features. We obtain good results based on a basic set of shallow features, thus showing that this method is a viable approach for economic event detection in news text
Template Mining for Information Extraction from Digital Documents
published or submitted for publicatio
Towards Building a Knowledge Base of Monetary Transactions from a News Collection
We address the problem of extracting structured representations of economic
events from a large corpus of news articles, using a combination of natural
language processing and machine learning techniques. The developed techniques
allow for semi-automatic population of a financial knowledge base, which, in
turn, may be used to support a range of data mining and exploration tasks. The
key challenge we face in this domain is that the same event is often reported
multiple times, with varying correctness of details. We address this challenge
by first collecting all information pertinent to a given event from the entire
corpus, then considering all possible representations of the event, and
finally, using a supervised learning method, to rank these representations by
the associated confidence scores. A main innovative element of our approach is
that it jointly extracts and stores all attributes of the event as a single
representation (quintuple). Using a purpose-built test set we demonstrate that
our supervised learning approach can achieve 25% improvement in F1-score over
baseline methods that consider the earliest, the latest or the most frequent
reporting of the event.Comment: Proceedings of the 17th ACM/IEEE-CS Joint Conference on Digital
Libraries (JCDL '17), 201
Econometrics meets sentiment : an overview of methodology and applications
The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software
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