activity
20182020
most citedEmotional Embeddings: Refining Word Embeddings to Capture Emotional Content of Words

14 citations · 27 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG20203 cited

A Novel Method of Extracting Topological Features from Word Embeddings

Shafie Gholizadeh, Armin Seyeditabari, Wlodek Zadrozny

In recent years, topological data analysis has been utilized for a wide range of problems to deal with high dimensional noisy data. While text representations are often high dimens…

cs.LG2020

Topological Data Analysis in Text Classification: Extracting Features with Additive Information

Shafie Gholizadeh, Ketki Savle, Armin Seyeditabari +1

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. As the prima…

cs.CL201910 cited

Emotion Detection in Text: Focusing on Latent Representation

Armin Seyeditabari, Narges Tabari, Shafie Gholizadeh +1

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, a…

cs.CL201914 cited

Emotional Embeddings: Refining Word Embeddings to Capture Emotional Content of Words

Armin Seyeditabari, Narges Tabari, Shafie Gholizade +1

Word embeddings are one of the most useful tools in any modern natural language processing expert's toolkit. They contain various types of information about each word which makes t…

cs.CL2018

Emotion Detection in Text: a Review

Armin Seyeditabari, Narges Tabari, Wlodek Zadrozny

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, a…