5,467 research outputs found
Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models
Volatility prediction--an essential concept in financial markets--has
recently been addressed using sentiment analysis methods. We investigate the
sentiment of annual disclosures of companies in stock markets to forecast
volatility. We specifically explore the use of recent Information Retrieval
(IR) term weighting models that are effectively extended by related terms using
word embeddings. In parallel to textual information, factual market data have
been widely used as the mainstream approach to forecast market risk. We
therefore study different fusion methods to combine text and market data
resources. Our word embedding-based approach significantly outperforms
state-of-the-art methods. In addition, we investigate the characteristics of
the reports of the companies in different financial sectors
Credibility Adjusted Term Frequency: A Supervised Term Weighting Scheme for Sentiment Analysis and Text Classification
We provide a simple but novel supervised weighting scheme for adjusting term
frequency in tf-idf for sentiment analysis and text classification. We compare
our method to baseline weighting schemes and find that it outperforms them on
multiple benchmarks. The method is robust and works well on both snippets and
longer documents
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