Empath

Abstract

Figure 1. Empath analyzes text across 200 gold standard topics and emotions (e.g., childishness or violence), and can generate and validate new lexical categories on demand from a user-generated set of seed terms. The Empath web interface highlights category counts for the current document (right). ABSTRACTHuman language is colored by a broad range of topics, but existing text analysis tools only focus on a small number of them. We present Empath, a tool that can generate and validate new lexical categories on demand from a small set of seed terms (like "bleed" and "punch" to generate the category violence). Empath draws connotations between words and phrases by deep learning a neural embedding across more than 1.8 billion words of modern fiction. Given a small set of seed words that characterize a category, Empath uses its neural embedding to discover new related terms, then validates the category with a crowd-powered filter. Empath also analyzes text across 200 built-in, pre-validated categories we have generated from common topics in our web dataset, like neglect, government, and social media. We show that Empath's data-driven, human validated categories are highly correlated (r=0.906) with similar categories in LIWC. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.

From physical to social interactions: The relative entropy model

“…Our next task was to compute the probability distributions of the topics for each participant and for the interactions of a given dyad. For the topic analysis, we have used Empath 15 , a software that performs automated topic analysis over a set of 194 pre-defined categories. Empath has been used in various contexts from suicide risk assessment 16 to DARPA'S Low Resources Languages for the Emergent Incidents project (LORELEI) 17 , and up to the identification of personality disorders 18 .…”

Mining Cross-Cultural Differences and Similarities in Social Media

“…Social Word Vocabulary Our social word vocabularies come from Empath (Fast et al, 2016) and OpinionFinder (Choi et al, 2005) for English, and TextMind (Gao et al, 2013) for Chinese. Empath is similar to LIWC (Tausczik and Pennebaker, 2009), but has more words and more categories and is publicly available.…”

Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study

“…We extracted features using lexicon based tools such as VADER (4 features) [23] , LIWC (70 features) [24] and Empath (195 features) [25] which have proven to be useful for characterizing social media text and extracting psychologically relevant signals. Features were also extracted from three pre-trained artificial neural network models: DeepMoji [26] was used to extract sentiment and emotion-related features (e.g., the use of emoticons in social media text); the Universal Sentence Encoder [27] v2 (using a Deep Averaging Network encoder) obtained from Tensorflow Hub which was specifically designed to facilitate transfer learning; and the Generative Pre-Trained network (GPT) v1 developed by OpenAI [19] .…”