Investor Reaction to Financial Disclosures across Topics: An Application of Latent Dirichlet Allocation*
نویسندگان
چکیده
منابع مشابه
Online Inference of Topics with Latent Dirichlet Allocation
Inference algorithms for topic models are typically designed to be run over an entire collection of documents after they have been observed. However, in many applications of these models, the collection grows over time, making it infeasible to run batch algorithms repeatedly. This problem can be addressed by using online algorithms, which update estimates of the topics as each document is obser...
متن کاملTopicXP: Exploring topics in source code using Latent Dirichlet Allocation
Acquiring general understanding of large software systems and components from which they are built can be a time consuming task, but having such an understanding is an important prerequisite to adding features or fixing bugs. In this paper we propose the tool, namely TopicXP, to support developers during such software maintenance tasks by extracting and analyzing unstructured information in sou...
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Data that has been annotated by linguists is often considered a gold standard on many tasks in the NLP field. However, linguists are expensive so researchers seek automatic techniques that correlate well with human performance. Linguists working on the ScamSeek project were given the task of deciding how many and which document classes existed in this previously unseen corpus. This paper invest...
متن کاملSpatial Latent Dirichlet Allocation
In recent years, the language model Latent Dirichlet Allocation (LDA), which clusters co-occurring words into topics, has been widely applied in the computer vision field. However, many of these applications have difficulty with modeling the spatial and temporal structure among visual words, since LDA assumes that a document is a “bag-of-words”. It is also critical to properly design “words” an...
متن کاملLatent Dirichlet Allocation
We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hofmann's aspect model , also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is generated as a mixture of topics, where t...
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ژورنال
عنوان ژورنال: Decision Sciences
سال: 2018
ISSN: 0011-7315,1540-5915
DOI: 10.1111/deci.12346