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Submit ticketScatterSpoke employs sentiment analysis to provide deeper insights into team feedback, helping to gauge overall morale and determine the intent behind the feedback.
Sentiment analysis, at its core, is the process of identifying and categorizing opinions expressed in text to determine the writer's emotional state or attitude. ScatterSpoke uses natural language processing (NLP) techniques to break down feedback from team members into smaller components for analysis.
ScatterSpoke uses sentiment lexicons—predefined lists of positive, negative, and neutral words. For example, words like "frustrating" or "excellent" are linked to negative and positive sentiments, respectively.
Our AI goes beyond surface-level word matching by understanding the context in which words are used. For example, the word "challenging" might be negative in some contexts but can indicate a positive sense of achievement in others.
ScatterSpoke’s AI doesn’t just assess sentiment; it works to understand intent by evaluating the tone, structure, and context of the feedback:
Positive Feedback: Praise or acknowledgement for good work (e.g., “The team worked excellently this sprint”) indicates a strong, motivated team.
Neutral Feedback: Comments that are objective or matter-of-fact (e.g., “We completed the project on time but need to review code quality”) often signal areas for improvement without strong emotional charge.
Negative Feedback: Constructive criticism, frustrations, or dissatisfaction (e.g., “The process is too slow and causing delays”) is flagged as negative and can reveal pain points.
Action-Oriented Intent: Feedback containing requests for change (e.g., “We should improve the code review process”) is categorized as suggesting improvement or identifying specific issues, regardless of sentiment.
ScatterSpoke’s sentiment analysis goes beyond individual feedback and aggregates data over time to track morale trends across teams:
Trend Analysis: The AI monitors sentiment data across multiple feedback sessions (e.g., retrospective meetings) to see if team morale is improving, stable, or declining. It can highlight shifts in sentiment tied to specific events, such as deadlines, new tool adoption, or organizational changes.
Sentiment by Topic: The system also tracks sentiment tied to specific areas such as workload, collaboration, or sprint goals. For instance, feedback on workload might consistently show a negative trend, indicating burnout or stress, while feedback on team collaboration could remain largely positive.
Alerts for Negative Trends: When a negative trend is detected—such as increased dissatisfaction or frustration—ScatterSpoke can notify team leads, allowing for timely intervention before small issues evolve into major problems.
ScatterSpoke’s sentiment analysis runs in real-time, providing instant insights into team dynamics and morale:
Interactive Dashboards: Teams can view sentiment breakdowns via ScatterSpoke’s dashboard, where feedback is categorized by sentiment (positive, neutral, negative) and intent (praise, criticism, suggestions).
Actionable Recommendations: Based on the sentiment analysis, ScatterSpoke can suggest actions to address morale issues, such as team-building activities or process adjustments, ensuring proactive engagement with team concerns.
As team feedback evolves, ScatterSpoke’s AI models continuously learn and improve. Over time, it becomes better at interpreting nuanced language, idiomatic expressions, and cultural differences in communication. For example, sarcastic feedback such as "Yeah, that meeting was super useful" would, over time, be correctly interpreted as negative sentiment.
By combining these sophisticated sentiment analysis techniques with intent detection, ScatterSpoke empowers teams to maintain high morale and address issues proactively, leading to healthier and more productive work environments.
ScatterSpoke helps you eliminate waste in your dev process.