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Found 7 papers, 1 papers with code
A New View of Multi-modal Language Analysis: Audio and Video Features as Text ``Styles''
Imposing the style of one image onto another is called style transfer.
Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions
We study the problem of medical symptoms recognition from patient text, for the purposes of gathering pertinent information from the patient (known as history-taking).
Shallow Domain Adaptive Embeddings for Sentiment Analysis
This paper proposes a way to improve the performance of existing algorithms for text classification in domains with strong language semantics.
Multi-modal Sentiment Analysis using Deep Canonical Correlation Analysis
This paper learns multi-modal embeddings from text, audio, and video views/modes of data in order to improve upon down-stream sentiment classification.
Using time series and natural language processing to identify viral moments in the 2016 U.S. Presidential Debate
Using a combined strategy of time series analysis and domain adapted word embeddings, this study provides an in-depth analysis of several key moments during the 2016 U. S. Presidential election.
Simple Algorithms For Sentiment Analysis On Sentiment Rich, Data Poor Domains.
SWESA leverages document label information to learn vector representations of words from a modest corpus of text documents by solving an optimization problem that minimizes a cost function with respect to both word embeddings and the weight vector used for classification.
Domain Adapted Word Embeddings for Improved Sentiment Classification
Generic word embeddings are trained on large-scale generic corpora; Domain Specific (DS) word embeddings are trained only on data from a domain of interest.
