Customer Reviews Semantic Analysis: Looker Studio Template

Raw CSV outputs from NLP APIs are difficult to interpret at scale—this four-page interactive Looker Studio dashboard transforms the semantic analysis results from Google Cloud NLP and IBM Watson NLU into actionable visual intelligence for customer review analysis. Built to work directly with the Excel export from Lazarina Stoy’s Customer Review Semantic Analysis notebook, these dashboards provide executive-ready visualizations across four analytical dimensions: entity sentiment, overall sentiment comparison, emotion distribution, and content moderation flags. The interactive design includes data exploration playgrounds with sliders and filters, allowing stakeholders to drill down into specific reviews by sentiment category, emotion type, entity mentions, or content safety concerns without technical expertise.

The dashboard suite consists of four interconnected pages, each addressing a distinct analytical question. The Entity Analysis & Entity Sentiment page reveals which specific products, features, locations, or aspects customers mention most frequently, ranking entities by mention count and showing their associated sentiment scores—instantly highlighting what customers talk about and whether they’re happy about it, with separate visualizations for top positive entities (what’s working) and top negative entities (what needs attention). The Sentiment Analysis page provides side-by-side comparison of Google Cloud NLP and IBM Watson NLU sentiment distributions using donut charts and stacked bars, revealing consensus and discrepancies between the two APIs—summary metrics at the top show total review counts by sentiment category (positive, neutral, negative) with granular breakdowns by intensity level (high, moderate, low). The Emotions Expressed page displays IBM Watson’s five-emotion analysis (sadness, joy, fear, disgust, anger) as both distribution charts and emotion timeline views, with the data exploration playground featuring individual sliders for each emotion to isolate reviews expressing specific psychological states. The Offensive or Sensitive Content page surfaces content moderation results from Google’s moderateText API, showing confidence scores across safety categories and highlighting reviews that may require human review before publication or response.

Use this for:
‧ Executive reporting on customer sentiment trends across product lines, time periods, or review sources with visual summaries that require no technical knowledge
‧ Product prioritization by identifying which entities (features, products, aspects) drive the most negative sentiment or appear most frequently in reviews
‧ Quality assurance and content moderation by filtering reviews flagged for potentially sensitive or inappropriate content before they’re published
‧ Emotional intelligence beyond sentiment by analyzing specific emotions (joy, fear, anger) to understand psychological drivers of customer satisfaction or frustration
‧ API validation and comparison by visually comparing how Google Cloud NLP and IBM Watson NLU interpret the same review corpus differently
‧ Targeted response strategies by using interactive filters to identify specific review segments (e.g., “highly negative reviews mentioning ‘customer service’”) for immediate action
‧ Data-driven customer experience improvements by correlating specific entity mentions with sentiment scores to prioritize feature enhancements or service improvements

This is perfect for customer experience teams, product managers, and marketing leaders who need to understand customer feedback at scale through visual dashboards rather than spreadsheets—particularly valuable when analyzing hundreds or thousands of reviews across multiple products, time periods, or platforms where manual analysis is impossible and stakeholders need to explore the data interactively without SQL or Python knowledge.