# Receptiviti Documentation > Our custom-built models are powered by proprietary language psychology science, helping you uncover insights about people to enhance predictive models, guide decisions, and improve interpersonal interactions. ## Overview - [Overview](https://docs.receptiviti.com/overview.md): What is Receptiviti? - [Receptiviti API Dashboard](https://docs.receptiviti.com/overview/dashboard.md): The dashboard is the main user interface to find information about your account. - [CSV Upload Tool](https://docs.receptiviti.com/overview/dashboard/csv-upload-tool.md): This is a basic user interface we provide where you can upload a CSV file of text to have it scored and returned as a separate CSV. The CSV upload tool is found in your account dashboard. You can initiate an account by going here. - [Custom Norming Contexts from Dashboard](https://docs.receptiviti.com/overview/dashboard/custom-norming-from-dashboard.md): This guide walks you through creating and managing custom norming contexts from your user Dashboard. - [Getting Started](https://docs.receptiviti.com/overview/getting-started.md): Calling the API - [Word Count Guidelines](https://docs.receptiviti.com/overview/word-count-guidelines.md): This table outlines the minimum word count requirements for generating reliable psycholinguistic insights across Receptiviti and LIWC measures. These word count thresholds reflect the amount of text needed to produce statistically valid and interpretable results. Shorter texts may yield noisy or incomplete outputs, while meeting or exceeding ideal counts enhances reliability, particularly for nuanced psychological interpretation. In general, more language yields more robust the analysis. ## Frameworks - [Frameworks](https://docs.receptiviti.com/frameworks.md): Receptiviti’s measures can be separated into two categories: proportional measures and normed measures. This is an important distinction for those who are looking to combine and compare multiple measures for the purpose of extracting insights. - [Cognition](https://docs.receptiviti.com/frameworks/normed-frameworks/cognition.md): Receptiviti’s Cognition framework provides access to nine measures that quantify levels of multiple aspects of cognitive processing and analytical thinking. - [Drives](https://docs.receptiviti.com/frameworks/normed-frameworks/drives.md): Receptiviti’s Drives framework contains six measures that provide insight into what motivates people. Drives can be strong predictors of individual or group behaviour, offering insight into whether a person is driven by a need for achievement and self actualization, a need for domination, a need for reward, or a focus on risk. - [Interpersonal Circumplex](https://docs.receptiviti.com/frameworks/normed-frameworks/interpersonal-circumplex.md): The Interpersonal Circumplex displays users' language on the axes based on scores from our agentic and communal measures. - [Needs and Values](https://docs.receptiviti.com/frameworks/normed-frameworks/needs-and-values.md): Receptiviti’s Needs and Values framework comprises 17 measures that evaluate aspects of what motivates a person’s preferences, habits, and decision-making. - [Personality - Big 5](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-big-5.md): The Big Five, also known as the Five Factor Model, is one of the dominant factor models of personality in psychology today. It proposes that every aspect of how we see each other and ourselves can be organized into five theoretically independent clusters of characteristics, called traits. These traits are as follows (see Schmidt et al., 2007): - [Personality - DISC](https://docs.receptiviti.com/frameworks/normed-frameworks/personality-disc.md): DISC is a versatile psychological framework designed to help leaders understand how people in groups relate to their peers and collaborate with each other. Receptiviti’s DISC measures require that text samples contain at least 350 words to generate results. - [Social Dynamics](https://docs.receptiviti.com/frameworks/normed-frameworks/social-dynamics.md): Receptiviti’s Social Dynamics framework provides access to seven measures that evaluate a number of important aspects of how people are focused on themselves, focused on other people, whether they communicate with authenticity, clout, hesitation, the degree to which they communicate formally or informally, and more. - [Thinking Fast and Slow](https://docs.receptiviti.com/frameworks/normed-frameworks/thinking-fast-and-slow.md): Receptiviti’s Thinking Fast and Slow framework is adapted from the dual systems model of cognition to measure two fundamental thinking modes: Slow thinking (effortful, careful, incremental) and Fast thinking (intuitive, reflexive, holistic). Thinking Fast and Slow is also known as System One and System Two thinking. Both modes of thinking have implications for the nature, quality, and speed of decision-making and reasoning. These measures are crucial for understanding how people think in various scenarios, including market research, audience segmentation, personnel selection, and leadership assessment. Neither way of thinking is inherently superior or inferior. Without flexible use of both modes of thought, it would be nearly impossible to effectively process our environments or make decisions. - [Toxicity](https://docs.receptiviti.com/frameworks/other-frameworks/toxicity.md): Hate speech is a serious and growing problem for online publishers, e-gaming companies, comment moderation platforms, and social media sites. In addition to the ethical reasons for combating online hate speech, governments across the globe are beginning to implement new legislation that requires platforms to remove hateful content within hours or face significant financial penalties. - [Emotions](https://docs.receptiviti.com/frameworks/proportional-frameworks/emotions.md): Receptiviti’s Emotions engine, called SALLEE (Syntax-Aware LexicaL Emotion Engine; pronounced Sally), detects emotions and sentiment expressed in text. It is designed to score the emotions a person is expressing, which can include emotions they’re feeling in the present, emotions they've felt in the past or expect to feel in the future, or emotions they see or assume others are feeling. Each emotion can be seen as negative, neutral, or positive. - [LIWC](https://docs.receptiviti.com/frameworks/proportional-frameworks/liwc.md): Linguistic Inquiry and Word Count (LIWC) is the gold standard for research in the field of Language Psychology. Created by Dr. James W. Pennebaker at the University of Texas, the software was originally used to examine the therapeutic value of writing by analyzing the frequency of psychologically-relevant linguistic features of text. Since its inception, the various LIWC dimensions have been validated and addressed in published research, and LIWC has been the basis for over 25,000 academic publications in a variety of fields covering topics such as power dynamics, thinking styles, motivations, communication dynamics,personality, consumer behavior, group dynamics, culture, and interpersonal relationships, among others. - [LIWC Extension](https://docs.receptiviti.com/frameworks/proportional-frameworks/liwc-extension.md): Receptiviti’s LIWC Extension framework provides measures focused on understanding communication dynamics and determinants of interpersonal support. These measures are paired to review several opposing forces in communication style. For example, the demonstration of a low or high amount of empathy, or the use of agentic (ambitious) versus communal (caring) language. - [Temporal and Orientation](https://docs.receptiviti.com/frameworks/proportional-frameworks/temporal-and-orientation.md): Receptiviti’s Temporal and Orientation measures provide access to three measures of Temporal Orientation and two measures of Attentional Focus. Temporal and Orientation measures provide insight into whether a person’s language and thoughts are rooted in the past, present, or future, while the Attentional Focus measures provide insight into whether a person is focused on themselves or on external entities. ## Developer Resources - [Developer Resources](https://docs.receptiviti.com/developer-resources.md): Explore our developer packages designed to streamline your workflow: ## Visualization UI - [Getting Started](https://docs.receptiviti.com/visualization-ui.md): Learn how to log in and get started with a Receptiviti UI project. - [Creating a Project](https://docs.receptiviti.com/visualization-ui/creating-a-project.md): Learn how to create a Receptiviti UI project. - [Export to Highlights](https://docs.receptiviti.com/visualization-ui/export-to-highlights.md): Learn how to export highlights of a text sample in the Receptiviti UI. - [Menu](https://docs.receptiviti.com/visualization-ui/menu.md): Learn about the menu items in the Receptiviti UI. - [Saving Worksheets as Local Files](https://docs.receptiviti.com/visualization-ui/saving-worksheets-as-local-files.md): Learn how to save worksheets as local files in the Receptiviti UI. - [Template Gallery](https://docs.receptiviti.com/visualization-ui/template-gallery.md): Using templates in the Receptiviti UI. - [Visualizing with Charts and Graphs](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs.md): Learn how to use the Receptiviti UI to analyze your language. - [Bar Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/bar-charts.md): Learn how to use the Receptiviti UI's bar chart to analyze your language. - [Circumplex Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/circumplex-chart.md): Learn how to use the Receptiviti UI's Circumplex chart to analyze your language. - [DISC Quadrant Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/disc-chart.md): Learn how to use the Receptiviti UI DISC Quadrant chart to analyze your language. - [Emotions by Topic](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/emotions-by-topic.md): Learn how to use the Receptiviti UI's Emotions by Topic graph to analyze your language. - [Heatmap](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/heatmap.md): Learn how to use the Receptiviti UI's Heatmap to analyze your language. - [Highlights](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/highlights.md): Learn how to use the Receptiviti UI's Highlights chart to analyze your language. - [Language Style Matching Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/language-style-matching-chart.md): Learn how to use the Receptiviti UI Language Style Matching chart to analyze your language. - [Line Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/line-charts.md): Learn how to use the Receptiviti UI's line charts to analyze your language. - [Lollipop Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/lollipop-chart.md): Learn how to use the Receptiviti UI's Lollipop chart to analyze your language. - [Radar Chart](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/radar-chart.md): Learn how to use the Receptiviti UI's Score Radar chart to analyze your language. - [Scatterplot](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/scatterplot.md): Learn how to use the Receptiviti UI's scatterplot chart to analyze your language. - [Score Proportion](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/score-proportion.md): Learn how to use the Receptiviti UI's Score Proportion chart to analyze your language. - [Table](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/table.md): Learn how to use the Receptiviti UI's table to analyze your language. - [Topics by Emotion](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/topics-by-emotions.md): Learn how to use the Receptiviti UI's topics by emotion graph to analyze your language. - [Worksheets and Dashboards](https://docs.receptiviti.com/visualization-ui/visualizing-with-charts-and-graphs/worksheets-and-dashboards.md): Learn how to use the Receptiviti UI's worksheets and dashhboards to analyze your language. ## api-ref - [API Reference](https://docs.receptiviti.com/api-ref.md) ## Data Preparation - [Preparing Your Data](https://docs.receptiviti.com/data-preparation.md): This page will help you determine what text is valid and what could potentially skew your results. Our technology performs best when samples come from written or spoken language, including conversational language, formal or informal language from a variety of sources including blog posts, survey responses, social media posts, transcribed calls, short text samples, or text messages. - [Optimizing Outcomes by Manipulating Text](https://docs.receptiviti.com/data-preparation/optimizing-outcomes-by-manipulating-text.md): Depending on the level of insight you aim to produce, it can be necessary to take different approaches to structuring your text samples for analysis. For example, sentence-level analysis works best when you want to identify emotions related to key topics of interest. Or, you can split conversational language into chronological segments to evaluate linguistic progression over time. Below are several examples of different useful text concatenation methods: - [Remove Unwanted Mentions Programmatically](https://docs.receptiviti.com/data-preparation/remove-unwanted-mentions-programmatically.md): @Mentions ## FAQ - [FAQ](https://docs.receptiviti.com/faq.md): Frequently Asked Questions ## From Scores to Insights - [From Scores to Insights](https://docs.receptiviti.com/from-scores-to-insights.md): Once you have obtained language analysis scores, the next step is understanding how to use them effectively. This involves applying statistical methods and interpretation techniques to extract meaningful insights. Below are key approaches to consider: - [I Scored My Dataset - Now What?](https://docs.receptiviti.com/from-scores-to-insights/i-scored-my-data-now-what.md): Once you have used the Receptiviti API to analyze your language data, you can use a variety of statistical methods and tools to further explore and understand the nuances of your dataset. By applying techniques like z-scoring, rank norming, and statistical tests such as t-tests and ANOVAs, you can identify patterns, differences, and relationships within the data. - [Score Interpretation Guide](https://docs.receptiviti.com/from-scores-to-insights/score-interpretation-guide.md): Methods for Normed Measures ## Knowledge Base - [Knowledge Base](https://docs.receptiviti.com/knowledge-base.md): Advanced explanations and interpretive guidance on psycholinguistic and language-based insights. ## Norming and Base Rates - [Norming and Base Rates](https://docs.receptiviti.com/norming-and-base-rates.md): This section provides guidance on understanding and applying norming and base rates effectively. Establishing your own norms allows for tailored comparisons that reflect the specific populations or contexts relevant to your analysis. Using different norming contexts—such as industry-specific language, cultural variations, or genre-based benchmarks—enhances the precision and relevance of your insights. - [Custom Norming](https://docs.receptiviti.com/norming-and-base-rates/custom-norming.md): To gain meaningful insights from language analysis, it’s essential to interpret scores within the context that makes sense for the language source. While Receptiviti provides norming through the use of our extensive proprietary datasets, custom norming allows you to tailor the norms to your specific dataset or context. For instance, you can create norms based on a specific dataset to generate scores that are directly relevant to that particular data. Alternatively, you can establish norms that apply more broadly to a context, enabling analysis of new datasets within the same contextual framework even if they were not part of the original norming data. This flexibility ensures that normed measures better reflect either localized conditions or broader contextual trends, enhancing the relevance and accuracy of your insights. - [Streamlined Custom Norming](https://docs.receptiviti.com/norming-and-base-rates/custom-norming/streamlined-custom-norming.md): This guide provides a streamlined approach designed for users who want effective results without diving deep into code. While the Developer Resources offer greater flexibility, this method balances ease of use with reliable outputs, making it an option for those looking to implement custom norming. - [Norming](https://docs.receptiviti.com/norming-and-base-rates/norming.md): Norming Options - [Receptiviti Base Rates](https://docs.receptiviti.com/norming-and-base-rates/receptiviti-base-rates.md): The table below outlines base rates for Receptiviti and LIWC API measures. Base rates are the mean scores of a measure in a specific context. Because language is context dependent, base rates are not universally applicable (e.g., base rates calculated using spoken language should not be used as reference points when interpreting analysis of written language). ## Selecting Measures for Analysis - [Selecting Frameworks and Measures](https://docs.receptiviti.com/selecting-measures-for-analysis.md): This section provides tailored framework recommendations based on specific use cases to help you select the most appropriate tools for your project needs. - [Measure Bundles](https://docs.receptiviti.com/selecting-measures-for-analysis/measure-bundles.md): Measure Bundles are recommended groups of measures that each reflect different aspects of the same underlying psychological concept. The bundle approach is different from our frameworks, which usually involve algorithmic measures based on psychological models such as the Big Five or DISC. Bundles instead facilitate exploratory analyses by providing users with a menu of relevant measures that will help make sense of patterns of results. - [What's Your Receptiviti Use Case?](https://docs.receptiviti.com/selecting-measures-for-analysis/whats-your-receptiviti-use-case.md): Explore framework suggestions categorized by their strengths and intended use cases, making it easier to align your goals with the right technologies. ## Optional - [API Reference](https://docs.receptiviti.com/api-reference): Interactive reference for the Receptiviti API (v2). - [OpenAPI Specification (v2)](https://docs.receptiviti.com/api/v2.openapi.yaml): Machine-readable OpenAPI spec for the Receptiviti API (v2).