PlaceSpeak collects a wide variety of different types of data for reporting purposes. Clients have expressed interest in obtaining AI Reporting, so PlaceSpeak has been hard at work developing new AI solutions.
For example, there may be hundreds of comments in a Discussion Forum that can be analyzed using AI. A Summary can be distilled from Discussion comments together with analysis categories and data visualizations. PlaceSpeak will be integrating AI reports from Survey data and PlaceIt Comments as well. A full range of AI reporting is in development looking at both qualitative and quantitive data all geocoded.
@AI Summary Generation Process
Content Analysis:
The Application Programming Interface (API) analyzes the extracted data, focusing on key themes, opinions, and sentiments expressed by the community members.
It identifies common topics, concerns, and viewpoints, looking for patterns or frequently mentioned points.
Summary Generation:
Using natural language processing (NLP) techniques, the API generates a coherent summary that encapsulates the main points from the community discussion.
The summary is crafted to be concise yet comprehensive, aiming to cover the diverse opinions and key take-a-ways.
Recommendation Formulation:
Based on the analyzed data and identified sentiments, the API formulates a recommendation.
The recommendation aims to reflect the overall sentiment and provide actionable advice for the engagement official.
Output Construction:
The summary and recommendation are compiled into a plain text format, as requested.
Table Generation Process
Keyword Selection:
For each response, the API identifies one or more keywords that represent the main topics or themes of the response.
If multiple keywords are identified, they are joined with the
&character instead of commas.
Sentiment Analysis:
The API evaluates the sentiment of each response as positive, neutral, or negative. This is done using sentiment analysis models or algorithms.
Emotion Detection:
The API determines the reaction or emotion conveyed in each response (e.g., happy, sad, angry, excited). This involves analyzing the tone and content of the text.
Confidence Score Generation:
The API generates a confidence score for its analysis in percentage format. This score reflects the confidence in the accuracy of the sentiment and emotion detection.
Location Determination:
The API determines the location associated with each response based on the data in the CSV. This could involve extracting location information directly from the text or inferring it based on context.
Output Formatting:
The data is formatted as specified: with each row containing the keywords, sentiment, emotion, confidence score, and location.