Sentiment Analysis

User story: As a marketing team member at Legoland devlopment, I want to anaylze the sentiment of comments and other engagements on our posts, to know how to increase engagement and user interaction by 20%.

My Role: Sentiment & Topic Analysis Lead Objective: Analyze the sentiment and key discussion topics in Legoland’s social media comments and replies to uncover patterns that impact engagement. Deliver insights that improve content strategy, enhance community interaction, and preemptively address PR risks.

Sentiment & Topic Analysis for Legoland’s Social Media Engagement on social media isn’t just about numbers—it’s about understanding how people feel and what they talk about. As part of our initiative to increase Legoland’s social media engagement by 20%, my role focuses on Sentiment & Topic Analysis to uncover audience perceptions and key discussion themes.

By using Natural Language Processing (NLP) techniques such as VADER for sentiment analysis and LDA/BERT for topic modeling, I will analyze comments and replies to identify: ✅ Emotional tone (positive, neutral, negative) and its impact on engagement. ✅ Trending discussion topics (e.g., patient experiences, health concerns, community feedback). ✅ Potential PR risks by detecting spikes in negative sentiment. ✅ Correlations between sentiment, topics, and engagement levels to determine what content resonates most.

The results will contribute to our data-driven engagement strategy through: 📊 A sentiment & topic trends dashboard visualizing audience emotions and key conversations. 📄 An insights report identifying which topics drive the most engagement. 📈 Actionable content recommendations to optimize messaging and improve community interaction.

By aligning content with audience sentiment and interests, this analysis will help Legoland create more meaningful, impactful social media interactions while strengthening its relationship with the community.

1️⃣ Sentiment Analysis Purpose: Understand how the audience feels about Legoland’s content by classifying comments into positive, neutral, or negative categories.

Key Responsibilities: Extract audience sentiment: Use Natural Language Processing (NLP) to assess emotional tone in comments and replies.

Identify high-engagement sentiment trends: Determine which types of posts receive the most positive engagement and which trigger negative reactions.

Detect PR concerns: Monitor sentiment shifts over time to flag potential reputation risks.

Analyze sentiment fluctuations: Compare sentiment across different content types (e.g., health tips, event promotions, patient stories).

Insights & Outcomes: Which post types generate the most positive responses?

Are there recurring issues leading to negative sentiment spikes?

How does sentiment correlate with likes, shares, and comments?

How does audience perception shift over time?

2️⃣ Topic Modeling Purpose: Identify the key themes in audience discussions and uncover which topics drive engagement.

Key Responsibilities: Extract recurring topics: Use topic modeling techniques to find common themes in user interactions.

Categorize audience discussions: Identify major discussion areas such as health concerns, patient experiences, and event feedback.

Correlate topics with engagement: Determine which topics receive the most likes, shares, and comments.

Spot emerging trends: Track new discussion topics that could be leveraged for content strategy.

Insights & Outcomes: What health topics are most discussed?

Which topics generate high engagement, and which ones receive little interaction?

Are there topic clusters associated with negative sentiment spikes?

How do topic trends shift over time?

3️⃣ Engagement Impact Analysis Purpose: Measure how sentiment and topic trends directly influence engagement metrics to optimize future content.

Key Responsibilities: Link sentiment & topic trends to engagement data: Analyze how audience reactions impact interactions.

Identify high-impact content themes: Determine which emotional tones and topics generate the most engagement.

Assess negative engagement risks: Detect topics linked to declining interactions or audience dissatisfaction.

Develop predictive insights: Recommend content themes that are likely to perform well based on past trends.

Insights & Outcomes: What combination of sentiment and topics leads to high engagement?

Are negative sentiment trends driving audience disengagement?

What themes should be emphasized or avoided in future posts?

How can content be optimized to improve audience retention and interaction?

4️⃣ Deliverables 📊 Sentiment & Topic Trends Dashboard

Visual representation of positive vs. negative sentiment trends over time.

Engagement heatmaps showing which topics generate high interaction.

Filters to explore sentiment and topic patterns based on content type.

📄 Insights Report

Breakdown of key discussion topics and their impact on engagement.

Identification of PR risks from negative sentiment spikes.

Summary of high-engagement sentiment drivers to guide content strategy.

📈 Content Strategy Recommendations

Topics that should be leveraged more for higher engagement.

Sentiment trends that indicate content improvement opportunities.

Posting strategies based on audience sentiment preferences.

How This Helps Legoland ✔ Improves content engagement by focusing on topics and emotional tones that resonate with the audience. ✔ Prevents PR issues by proactively identifying negative sentiment trends. ✔ Provides a data-driven approach to crafting impactful, engaging social media content.