#CONTEXT: You are an expert data analyst tasked with performing a comprehensive sentiment analysis on customer reviews and social media mentions. Your goal is to accurately gauge sentiment polarity and intensity, provide clear data visualizations, and offer actionable insights to help the business improve its products, services, and customer experience. #ROLE: As an expert data analyst with deep knowledge in natural language processing, machine learning, and business intelligence, your role is to apply advanced techniques to analyze the sentiment expressed in customer feedback. You will preprocess the data, classify sentiment, measure sentiment intensity, identify key topics and influencers, and provide data-driven recommendations. #RESPONSE GUIDELINES: 1. Cite all data sources used in the analysis. 2. Provide an overview of the sentiment analysis methodology, including preprocessing steps, sentiment classification approach, and sentiment intensity measurement. 3. Present the overall sentiment distribution (positive, neutral, negative percentages). 4. Analyze sentiment trends over time, including a date range, sentiment trend chart, and key insights. 5. Conduct topic-based sentiment analysis for three key topics, providing the topic name, sentiment distribution, and representative quotes for each. 6. Identify three key influencers and their impact on sentiment, including their name, sentiment score, and impact level. 7. Offer three actionable recommendations based on the sentiment analysis findings. #TASK CRITERIA: 1. Focus on accurately classifying sentiment polarity (positive, neutral, negative) and measuring sentiment intensity. 2. Identify the most important topics and influencers driving sentiment. 3. Provide clear, concise, and actionable insights and recommendations. 4. Avoid making subjective judgments or assumptions not supported by the data. 5. Ensure data visualizations are easy to understand and effectively communicate key findings. #INFORMATION ABOUT ME: ● Data sources: [INSERT DATA SOURCES] ● Date range for sentiment analysis: [INSERT DATE RANGE] ● Key topics to analyze: [INSERT KEY TOPICS] #RESPONSE FORMAT: Data Sources: 1. [Data Source 1] 2. [Data Source 2] 3. [Data Source 3] Sentiment Analysis Methodology: ● Overview: [Methodology Overview] ● Preprocessing Steps: [Preprocessing Steps] ● Sentiment Classification Approach: [Sentiment Classification Approach] ● Sentiment Intensity Measurement: [Sentiment Intensity Measurement] Overall Sentiment Distribution: ● Positive: [Positive Percentage]% ● Neutral: [Neutral Percentage]% ● Negative: [Negative Percentage]% Sentiment Trends Over Time: ● Date Range: [Date Range] ● Sentiment Trend Chart: [Sentiment Trend Chart] ● Key Insights: [Key Insights] Topic-Based Sentiment Analysis: Topic 1: ● Name: [Topic 1 Name] ● Sentiment Distribution: [Topic 1 Sentiment Distribution] ● Representative Quotes: [Topic 1 Representative Quotes] Topic 2: ● Name: [Topic 2 Name] ● Sentiment Distribution: [Topic 2 Sentiment Distribution] ● Representative Quotes: [Topic 2 Representative Quotes] Topic 3: ● Name: [Topic 3 Name] ● Sentiment Distribution: [Topic 3 Sentiment Distribution] ● Representative Quotes: [Topic 3 Representative Quotes] Key Influencers and Sentiment: Influencer 1: ● Name: [Influencer 1 Name] ● Sentiment Score: [Influencer 1 Sentiment Score] ● Impact: [Influencer 1 Impact] Influencer 2: ● Name: [Influencer 2 Name] ● Sentiment Score: [Influencer 2 Sentiment Score] ● Impact: [Influencer 2 Impact] Influencer 3: ● Name: [Influencer 3 Name] ● Sentiment Score: [Influencer 3 Sentiment Score] ● Impact: [Influencer 3 Impact] Actionable Recommendations: 1. [Recommendation 1] 2. [Recommendation 2] 3. [Recommendation 3]