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Found 9 papers, 0 papers with code
Understanding Negations in Information Processing: Learning from Replicating Human Behavior
Information systems experience an ever-growing volume of unstructured data, particularly in the form of textual materials.
Statistical Inferences for Polarity Identification in Natural Language
Information forms the basis for all human behavior, including the ubiquitous decision-making that people constantly perform in their every day lives.
Investor Reaction to Financial Disclosures Across Topics: An Application of Latent Dirichlet Allocation
This paper provides a holistic study of how stock prices vary in their response to financial disclosures across different topics.
Reinforcement Learning in R
Reinforcement learning refers to a group of methods from artificial intelligence where an agent performs learning through trial and error.
Understanding the Role of Two-Sided Argumentation in Online Consumer Reviews: A Language-Based Perspective
This paper examines the effect of two-sided argumentation on the perceived helpfulness of online consumer reviews.
Sentence-Level Sentiment Analysis of Financial News Using Distributed Text Representations and Multi-Instance Learning
Our analysis of a manually-labeled dataset yields a predictive accuracy of up to 69. 90%, exceeding the performance of alternative approaches by at least 3. 80 percentage points.
The Longer the Better? The Interplay Between Review Length and Line of Argumentation in Online Consumer Reviews
In this paper, we propose that longer reviews should not be assumed to be uniformly more helpful; instead, we argue that the effect depends on the line of argumentation in the review text.
Integrating Floor Plans into Hedonic Models for Rent Price Appraisal
In this study, we investigate to what extent an automated visual analysis of apartment floor plans on online real estate platforms can enhance hedonic rent price appraisal.
Which linguistic cues make people fall for fake news? A comparison of cognitive and affective processing
However, little is known about what linguistic cues make people fall for fake news and, hence, how to design effective countermeasures for social media.
