I have a CSV file containing data from a multiple choice survey asking participants to indicate how a group of brand colors make them feel. The survey data includes quantitative ratings and qualitative explanations. I need you to analyze this data and perform the following tasks:
**Calculate the Sentiment score:**
Use the responses from **[column name]** in the CSV to calculate NPA, using the following formula:
Sentiment score = (Sum of Positive Impressions) ➗ (Sum of All Impressions)
• **Sum of Positive Impressions** = percentage of participants who selected **[Impression 1]**, **[Impression 2]**, **[Impression 3]**, or **[Impression 4]**.
• **Sum of All Impressions** = percentage of participants who selected positive impressions above and the following negative impressions: **[Impression 5]**, **[Impression 6]**, **[Impression 7]**, or **[Impression 8]**.
Each impression type should be represented as a percentage of total responses, and the Sentiment score should be expressed as a percentage by dividing the positivie impression sum by the total impression sum.
**Identify Improvements:**
Based on the results of the quantitative feelings choices in **[column name]** and the qualitative feedback in **[column name]**, suggest at least three improvements.
Please process the data, execute the analysis, and generate a report summarizing the findings and recommendations in a structured formate any relevant statistical summaries, graphs, or visualizations that might enhance.
Summary
N (answered Section 4 Choices): 102
Per-impression selection rates (percent of respondents):
Positive: Successful 60.78%, Relevant 55.88%, Diverse 42.16%, Determined 35.29%
Negative: Boring 6.86%, Unprofessional 3.92%, Confusing 2.94%, Unrealistic 2.94%
Sum of Positive Impressions (Σ%): 194.11
Sum of All Impressions (Σ% of positives + negatives): 210.78
Sentiment score (NPA):
194.11
210.78
=
92.07
%
210.78
194.11
=92.07%
You’ll see two charts above:
Per-impression selection rates
Positive vs negative impression sums
And a tidy table you can download:
Download the summary CSV
Why sums can exceed 100%: participants can select multiple impressions; each impression is computed as % of respondents who selected it.
What participants said (Section 4 Explanation)
Common terms & phrases focused on images, success, diversity, and how the page feels:
Top words: page, images, people, success, information, diverse, determined, feel
Frequent bigrams: “look like,” “people different,” “images show,” “feel determined,” “make feel”
Representative quotes
“Seeing an image of a successful student.”
“It shows a diverse student ratio and it makes people determined to go back to school.”
“Relevant because everything you need is here… Boring because of the long landing page.”
“Considering it’s an online college, that makes it confusing.”
“The look is one I’ve clearly seen before… would be more formal with smaller font size and black, not gray.”
Takeaways: The palette and imagery largely communicate success, relevance, diversity, and determination. A minority perceive the page as boring (length/scroll fatigue) or confusing (information density, layout).
Recommendations
Keep the winners visible (Successful, Relevant) — raise Determined further
Swap at least one static hero shot for an action-oriented scene (students building, presenting, collaborating).
Use verb-led headlines and progress cues (e.g., “Apply → Plan → Start”) to prime determination.
Reduce “Boring” (6.86%) by tightening the long page
Break the landing page into clear sections with anchor links (“Programs,” “Costs,” “Outcomes”).
Add a lightweight sticky summary bar (programs | tuition | outcomes | apply) to reduce scroll burden.
Replace repetitive static images with short looping clips or varied scenarios.
Lower “Confusing/Unprofessional/Unrealistic” (~10.8% combined) via clarity & credibility
Improve typographic hierarchy and contrast; avoid low-contrast gray for core copy.
Pair visuals with concrete captions (“Capstone demo day • Cohort 2025”).
Add micro-proof near CTAs (placement stats, alumni logos, “no credit card required”).
Amplify Diversity without tokenism
Ensure role, age, and body-type diversity (not only ethnicity); connect imagery to real contexts (labs, internships, online collaboration).
Use alt text and captions that explain the scene and avoid stereotypes.
Validate changes quickly
Re-run the same sentiment checklist; target NPA ≥ 93%, Boring ≤ 5%, Determined ≥ 40%.
Add a 5-second test on the revised hero to confirm message clarity ≥ 75%.