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2017 EMNLP EMNLP 2017

DLATK: Differential Language Analysis ToolKit

Abstract

AbstractWe present Differential Language Analysis Toolkit (DLATK), an open-source python package and command-line tool developed for conducting social-scientific language analyses. While DLATK provides standard NLP pipeline steps such as tokenization or SVM-classification, its novel strengths lie in analyses useful for psychological, health, and social science: (1) incorporation of extra-linguistic structured information, (2) specified levels and units of analysis (e.g. document, user, community), (3) statistical metrics for continuous outcomes, and (4) robust, proven, and accurate pipelines for social-scientific prediction problems. DLATK integrates multiple popular packages (SKLearn, Mallet), enables interactive usage (Jupyter Notebooks), and generally follows object oriented principles to make it easy to tie in additional libraries or storage technologies.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Interdisciplinary and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — psychological analysis
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

H. Andrew Schwartz , Salvatore Giorgi , Maarten Sap , Patrick Crutchley , Lyle Ungar , Johannes Eichstaedt

Topics

Artificial Intelligence > Core AI > Interpretability Machine Learning > Core Methods > Classification Natural Language Processing > Applications > Text Classification Interdisciplinary > Social > Social Media Analysis Artificial Intelligence > Core AI > Natural Language Processing

Keywords

sentiment analysis natural language processing text classification social media analysis support vector machine social science psychological analysis
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