Receptiviti

Receptiviti, developed by Receptiviti Inc., is a language-based psychological insights platform that utilizes natural language processing (NLP) and machine learning to analyze text data and provide valuable psychological insights. It aims to help businesses and organizations gain a comprehensive understanding of individuals' personality traits, emotions, motivations, and communication styles. Receptiviti offers a range of frameworks and measures that can be applied to various use cases, including talent assessment, workforce culture analysis, leadership style assessment, mental wellness monitoring, social media psychology, and more.

Key Features

Big Five Personality: Receptiviti measures personality traits based on the Big Five model, providing insights into an individual's personality characteristics and their potential impact on behavior and decision-making. This feature enables businesses to understand their employees' personality profiles and tailor strategies accordingly.

DISC Personality: Receptiviti utilizes the DISC model to assess individual differences in emotional and communication styles, helping businesses understand how individuals prefer to work, communicate, and interact with others.

Needs and Values: Receptiviti evaluates the motivations and values that drive an individual's preferences, habits, and decision-making, providing insights into the factors that influence their choices and behaviors.

Emotions: Receptiviti analyzes language to detect and measure specific emotions and sentiment, helping businesses understand how individuals feel about products, services, and important topics, enabling them to tailor their messaging accordingly.

Cognition: Receptiviti measures multiple aspects of cognitive processing and analytical thinking, providing insights into how individuals think, process information, problem-solve, and make decisions.

Social Dynamics: Receptiviti evaluates important aspects of how individuals interact with others, such as authenticity, clout, confidence, formality, and self-absorption, providing insights into how individuals engage in social interactions and communicate with others.

Drives and Motivations: Receptiviti assesses the motivations and drivers of behavior, helping businesses understand what motivates individuals and predicts their behavior, enabling tailored strategies to engage and motivate employees or customers.

Communication Dynamics (LIWC Extension): Receptiviti focuses on understanding communication dynamics and determinants of interpersonal support, measuring empathy, agentic versus communal language, abstractness, concreteness, and avoidance. This feature is useful for analyzing conversational language in therapeutic scenarios, interviews, job postings, and corporate communications.

Toxicity: Receptiviti detects the probability of toxic language in text data, including threats, hate speech, offensive language, and general toxicity, providing insights into the specific sources of toxicity and helping businesses identify and address harmful or inappropriate language in their communications.

LIWC (Linguistic Inquiry and Word Count): Receptiviti analyzes the psychological meaning of word use in text data, classifying language into 94 psychologically-relevant categories. This widely used feature helps understand patterns in social relationships, cognitive processes, motivations, and more across various research fields.