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Found 7 papers, 1 papers with code
A Novel Method of Extracting Topological Features from Word Embeddings
In this paper, we introduce a novel algorithm to extract topological features from word embedding representation of text that can be used for text classification.
Topological Data Analysis in Text Classification: Extracting Features with Additive Information
While the strength of Topological Data Analysis has been explored in many studies on high dimensional numeric data, it is still a challenging task to apply it to text.
Emotion Detection in Text: Focusing on Latent Representation
In recent years, emotion detection in text has become more popular due to its vast potential applications in marketing, political science, psychology, human-computer interaction, artificial intelligence, etc.
Emotional Embeddings: Refining Word Embeddings to Capture Emotional Content of Words
We show that fitting an emotional model into pre-trained word vectors can increase the performance of these models in emotional similarity metrics.
Causality Analysis of Twitter Sentiments and Stock Market Returns
Sentiment analysis is the process of identifying the opinion expressed in text.
Emotion Detection in Text: a Review
In recent years, emotion detection in text has become more popular due to its vast potential applications in marketing, political science, psychology, human-computer interaction, artificial intelligence, etc.
SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets
Sentiment analysis is the process of identifying the opinion expressed in text.
