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Found 6 papers, 5 papers with code
Multi-task Learning of Negation and Speculation for Targeted Sentiment Classification
The majority of work in targeted sentiment analysis has concentrated on finding better methods to improve the overall results.
FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms
We present FIESTA, a model selection approach that significantly reduces the computational resources required to reliably identify state-of-the-art performance from large collections of candidate models.
Bringing replication and reproduction together with generalisability in NLP: Three reproduction studies for Target Dependent Sentiment Analysis
Lack of repeatability and generalisability are two significant threats to continuing scientific development in Natural Language Processing.
Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines
This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1.
Learning Tone and Attribution for Financial Text Mining
Previous studies have only applied manual content analysis on a small scale to reveal such a bias in the narrative section of annual financial reports.
Very Fast EM-based Mixture Model Clustering using Multiresolution kd-trees
Clustering is important in many fields including manufacturing, biology, finance, and astronomy.
