The Joyful Insights Forecasting (AI) API delivers emotional classification results, categorizing input data as either 'Happy' or 'Unhappy'. Utilizing advanced machine learning techniques, this API specializes in sentiment analysis to provide accurate predictions. For further details about the underlying model and its capabilities, please reach out to us.
{
"Value Prediction": "Happy"
}
curl --location --request GET 'https://zylalabs.com/api/6822/joyful+insights+forecasting+ai+api/10309/hapiness?X1=Required&X2=Required&X3=Required&X4=Required&X5=Required&X6=Required' --header 'Authorization: Bearer YOUR_API_KEY'
{
"X1": "the availability of information about the city services",
"X2": "the cost of housing",
"X3": "the overall quality of public schools",
"X4": "your trust in the local police",
"X5": "the maintenance of streets and sidewalks",
"X6": "the availability of social community events"
}
curl --location --request GET 'https://zylalabs.com/api/6822/joyful+insights+forecasting+ai+api/10310/listallvars' --header 'Authorization: Bearer YOUR_API_KEY'
{
"API Info:": "This API requests per values provide class result ('Happy' or 'Unhappy'). The AI algorithm consists of ML (Machine Learning) techniques for Classification task. Please contact us for more information about the model. We acknowledge the researchers involved in this study and thank them for providing the Dataset for Analysis. We here cite the dataset: 'Koczkodaj, W. (2017). Somerville Happiness Survey [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5PW36.'"
}
curl --location --request GET 'https://zylalabs.com/api/6822/joyful+insights+forecasting+ai+api/10311/info' --header 'Authorization: Bearer YOUR_API_KEY'
Após se cadastrar, cada desenvolvedor recebe uma chave de acesso à API pessoal, uma combinação única de letras e dígitos para acessar nosso endpoint de API. Para autenticar com a Joyful Insights Forecasting AI API basta incluir seu token Bearer no cabeçalho Authorization.
| Cabeçalho | Descrição |
|---|---|
Authorization
|
Obrigatório
Deve ser Bearer access_key. Veja "Sua chave de acesso à API" acima quando você estiver inscrito.
|
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This API request provides a class result ('Happy' or 'Unhappy') based on a requested mini-survey with answers ranging from 1 (Totally Disagree) to 5 (Totally Agree). The AI algorithm consists of ML (Machine Learning) techniques for the Classification task. For more information about the model, please contact us. We acknowledge the researchers involved in this study and thank them for providing the Dataset for Analysis. We here cite the dataset: 'Koczkodaj, W. (2017). Somerville Happiness Survey [Dataset]. UCI Machine Learning Repository.'
The "hapiness" endpoint returns a classification result indicating emotional state as either 'Happy' or 'Unhappy'. The "listallvars" endpoint provides a list of variables related to factors influencing happiness, while the "info" endpoint offers general information about the API and its underlying model.
The "hapiness" endpoint response includes "Value Prediction" indicating the emotional classification. The "listallvars" response contains variable names (e.g., "X1", "X2") and their descriptions, while the "info" endpoint provides a summary of the API's functionality and data sources.
Responses are structured in JSON format. For "hapiness", it returns a single key-value pair. The "listallvars" endpoint returns multiple key-value pairs for each variable, and the "info" endpoint provides a comprehensive overview in a single JSON object.
The "hapiness" endpoint provides emotional classifications, the "listallvars" endpoint details various factors affecting happiness, and the "info" endpoint gives insights into the API's functionality and data sources.
Currently, the API does not support customizable parameters for the "hapiness" or "listallvars" endpoints. Users can simply call these endpoints to receive the predefined data. Future enhancements may allow for more tailored requests.
The data utilized by the Joyful Insights Forecasting AI API is based on the Somerville Happiness Survey dataset, provided by Koczkodaj, W. (2017). This dataset is available in the UCI Machine Learning Repository, ensuring a reliable source for analysis.
Typical use cases include sentiment analysis for social research, understanding community well-being, and enhancing user experience in applications that require emotional insights. The data can help organizations gauge public sentiment on various issues.
Users can leverage the "hapiness" classification to assess emotional trends in their data, while the "listallvars" information can guide them in identifying key factors influencing happiness. This can inform decision-making in community planning or service improvements.