AI Driven Sentiment Analysis Workflow for Post Date Feedback

Sentiment Analysis for Post-Date Feedback

1. Data Collection

1.1. User Feedback Submission

Users submit feedback through an integrated platform post-date, utilizing a structured questionnaire that includes qualitative and quantitative questions.

1.2. Data Sources

Collect feedback from various sources including:

• In-app surveys
• Email follow-ups
• Social media interactions

2. Data Processing

2.1. Data Cleaning

Utilize AI-based tools to clean and preprocess the collected data, removing duplicates and irrelevant entries. Tools such as Apache Spark can be employed for large-scale data processing.

2.2. Sentiment Analysis Implementation

Apply natural language processing (NLP) techniques to analyze user feedback. Tools like Google Cloud Natural Language API or IBM Watson Natural Language Understanding can be used to extract sentiment scores from text.

3. Data Analysis

3.1. Sentiment Scoring

Classify feedback into positive, negative, or neutral categories based on the sentiment analysis results. This can be achieved using machine learning algorithms such as Support Vector Machines (SVM) or Naive Bayes.

3.2. Trend Analysis

Identify trends in user sentiment over time. Visualization tools like Tableau or Power BI can help present the data effectively.

4. Reporting

4.1. Insights Generation

Generate comprehensive reports summarizing user sentiment, highlighting areas of improvement and satisfaction. AI-driven reporting tools such as Looker can automate this process.

4.2. Stakeholder Presentation

Present findings to stakeholders through interactive dashboards and presentations, ensuring actionable insights are clearly communicated.

5. Actionable Feedback Loop

5.1. Recommendations for Improvement

Based on sentiment analysis, provide actionable recommendations to enhance user experience. This may include adjustments in matchmaking algorithms or user interface enhancements.

5.2. Continuous Monitoring

Establish a continuous feedback loop by regularly updating the sentiment analysis model and adapting to new data. Implement tools like Amazon SageMaker for ongoing model training and improvement.

6. User Engagement

6.1. Personalized Follow-ups

Utilize AI to send personalized follow-up messages to users, addressing their feedback and enhancing user engagement. Platforms like Drift can facilitate automated communication.

6.2. Community Building

Foster a community around user feedback, utilizing forums and discussion boards to encourage ongoing interaction and support.