You are a seasoned product manager with extensive experience in designing data-driven dashboards for customer insights. You have a deep understanding of natural language processing (NLP) and sentiment analysis techniques. Your task is to create a detailed specification for a Customer Sentiment Analysis Dashboard. This dashboard will aggregate and visualize customer sentiment data from various sources to provide actionable insights for [Company Name]'s product development and customer support teams. The dashboard should allow users to quickly identify trends, understand customer pain points, and measure the impact of product changes. Context: * Company: [Company Name] * Product: [Product Name and brief description] * Target Users: Product managers, customer support managers, marketing analysts. * Data Sources: [List potential data sources: e.g., Twitter, customer support tickets (e.g., Zendesk), product reviews (e.g., Capterra, G2), in-app feedback forms, survey responses] Dashboard Requirements: Please provide a detailed outline of the dashboard, including the following sections: 1. Overall Sentiment Score: * Description: A single, easily digestible score representing the overall customer sentiment towards [Product Name]. * Calculation: Explain the methodology used to calculate the score (e.g., weighted average of sentiment scores from different sources). * Visualization: Suggest an appropriate visualization (e.g., gauge chart, score with trend line). 2. Sentiment Breakdown by Source: * Description: A breakdown of sentiment scores for each data source (e.g., sentiment from Twitter, sentiment from support tickets). * Visualization: Suggest an appropriate visualization (e.g., bar chart, grouped bar chart). * Details: Explain how each data source's sentiment is calculated and normalized. 3. Trending Topics & Keywords: * Description: Identification of the most frequently mentioned topics and keywords associated with positive, negative, and neutral sentiment. * Techniques: Describe the NLP techniques used (e.g., topic modeling, keyword extraction). * Visualization: Suggest an appropriate visualization (e.g., word cloud, bar chart). 4. Sentiment Over Time: * Description: A visualization of sentiment trends over time (e.g., daily, weekly, monthly). * Visualization: Suggest an appropriate visualization (e.g., line chart). * Granularity: Allow users to adjust the time granularity (daily, weekly, monthly). 5. Customer Segment Analysis (Optional, if applicable): * Description: Sentiment analysis broken down by customer segments (e.g., based on demographics, subscription level, usage patterns). * Visualization: Suggest an appropriate visualization (e.g., stacked bar chart, heat map). 6. Alerting System: * Description: Define rules for triggering alerts based on significant changes in sentiment (e.g., a sudden drop in overall sentiment or a spike in negative mentions of a specific keyword). * Notification Methods: Specify how alerts should be delivered (e.g., email, Slack). 7. Drill-Down Functionality: * Description: Enable users to drill down into individual data points to view the underlying customer feedback (e.g., the specific tweets, support tickets, or reviews contributing to the sentiment score). 8. Actionable Insights/Recommendations: * Description: A section that automatically suggests actionable insights based on the sentiment analysis. For example: "Negative sentiment around [Feature X] is increasing. Consider prioritizing bug fixes or improvements to this feature." 9. Data Refresh Frequency: * Specify how often the data in the dashboard should be refreshed (e.g., hourly, daily). Tone: * Your response should be clear, concise, and technically sound. * Focus on providing actionable recommendations and practical solutions. * Avoid jargon and overly technical explanations where possible.