Review Sentiment Aggregator

1. Overview

The Review Sentiment Aggregator reads a set of customer reviews and produces a concise summary that tells you the overall feeling (sentiment) of the customers and highlights the most common pain‑points they mention.

2. Business Value

Customer Insight – Quickly see how customers feel about the product.
Prioritization – Identify the most frequent problems so the product team can focus on fixing what matters most.
Efficient Reporting – Provides a ready‑to‑share summary for product managers, marketers, and leadership without manual analysis.

3. Operational Context

When to run – After a product release, a marketing campaign, or any period when a batch of customer feedback has been collected.
Who uses it – Product managers, product owners, and marketing analysts who need a quick, data‑driven view of customer sentiment.
How often – As often as new review data is available (e.g., weekly, after each launch, or on a scheduled cadence).

4. Inputs

4.1 List of Customer Reviews

Field	Type	Details Provided
Review ID	Text (human‑readable)	A short, unique identifier for the review (e.g., “R001”).
Review Text	Text	The full text of the customer’s comment or review.

Only the above two fields are required for each review.

Note: If any review is missing its text, that review will be excluded from analysis and flagged for manual review (see Section 8).

5. Outputs

5.1 Sentiment Summary Report

Item	Content & Format
Overall Sentiment	Text – “Positive”, “Negative”, or “Mixed” based on the majority of reviews.
Sentiment Breakdown	Bullet list showing the count and percentage of Positive, Neutral, and Negative reviews (e.g., “Positive: 8 (53 %)”).
Top Pain Points	Ranked list (up to 5) of the most frequently mentioned problems, each with a brief description and the number of times it appeared.
Notes	Any observations, such as “2 reviews could not be processed due to missing text”.

Formatting Rules

Use plain, concise language.
Use bullet points for lists.
Keep each item on its own line.
Do not generate any new identifiers (e.g., “ID‑001”).

6. Detailed Plan & Execution Steps

Verify Input
- Confirm the list of reviews contains at least one entry.
- If any entry is missing the Review Text, record the Review ID in a “Missing Text” list and remove that entry from further processing.
Assign Sentiment to Each Review
- Scan the Review Text for words and phrases from the Sentiment Keyword List (Appendix C).
- If more positive words than negative words are found, label the review Positive.
- If more negative words than positive words are found, label it Negative.
- If the count is equal or no clear words are found, label it Neutral.
Extract Potential Pain Points
- Search the Review Text for any phrase that matches a Pain‑Point Keyword (e.g., “slow”, “crash”, “missing”, “confusing”, “hard to use”).
- Record each distinct phrase as a candidate pain‑point.
Aggregate Pain‑Points
- Normalize similar phrases (e.g., “slow loading” and “slow performance” become “Performance”) using the Pain‑Point Normalization Table (Appendix C).
- Count how many times each normalized pain‑point appears across all reviews.
Rank Pain Points
- Sort the aggregated pain‑points by frequency, descending.
- Take the top‑5 entries (or fewer if less than five exist).
Calculate Overall Sentiment
- Count the number of Positive, Negative, and Neutral reviews.
- Determine the majority label:
  - If Positive > Negative and Positive > Neutral → Overall Sentiment = Positive
  - If Negative > Positive and Negative > Neutral → Overall Sentiment = Negative
  - Otherwise → Overall Sentiment = Mixed.
Compose the Summary Report
- Write Overall Sentiment as a single line.
- Provide Sentiment Breakdown as a bullet list (e.g., “Positive: 7 (58 %)”).
- List Top Pain Points in ranked order with count (e.g., “1. Slow performance – 5 mentions”).
- Add a Notes section with any anomalies (e.g., number of excluded reviews).
Produce Output
- Assemble the report exactly as described in Section 5.1.
- Return the Sentiment Summary Report as plain text (no files, no IDs).

7. Validation & Quality Checks

Input Presence – Ensure at least one review with non‑empty text is present; otherwise, abort and flag for manual review.
Sentiment Consistency – Re‑run the word‑count check for each processed review to verify that the assigned label matches the keyword count.
Pain‑Point Normalization – Verify that each extracted phrase matches a term in the Pain‑Point Keyword List; if not, flag as “Unrecognized” and list in notes.
Report Completeness – Confirm that the report includes all four components (Overall Sentiment, Sentiment Breakdown, Top Pain Points, Notes).
Count Accuracy – Verify that the sum of Positive, Neutral, and Negative counts equals the total number of processed reviews.

8. Special Rules / Edge Cases

Missing Review Text – Exclude the review, note the Review ID in “Notes”, and continue processing remaining reviews.
Tie for Top Pain Points – If more than five pain‑points share the same frequency and exceed the limit, include all tied items up to the limit of five; if the limit would be exceeded, list the first five alphabetically.
All Reviews Neutral – Set Overall Sentiment = Mixed and note “All reviews neutral”.
No Pain Points Identified – Report “No significant pain‑points detected” in the Top Pain Points section.
Ambiguous Sentiment – If a review contains equal numbers of positive and negative keywords, label it Neutral.

9. Example

Input (list of 3 reviews)

Review ID	Review Text
R001	“The new dashboard looks great, but the loading time is painfully slow.”
R002	“I love the new features, but the app crashes often when I try to export a report.”
R003	“Excellent performance! Works exactly as I expected.”

Output (Sentiment Summary Report)

Overall Sentiment: Mixed
Sentiment Breakdown
- Positive: 2 (66 %)
- Negative: 1 (33 %)
- Neutral: 0 (0 %)
Top Pain Points
1. Slow loading – 1 mention
2. Frequent crashes – 1 mention
Notes
- No missing review text.
- All three reviews processed successfully.

Appendix A – FAQ

Can this process handle reviews in other languages?
- The current implementation only looks for English keywords. Non‑English reviews will be treated as Neutral unless they contain English keywords.
What if a review contains multiple pain‑points?
- All distinct pain‑points found in a single review are counted separately.
Do I need to pre‑process the reviews (e.g., remove HTML tags)?
- Yes. Reviews should be plain text without markup.
What if the sentiment keywords list is too short?
- The process will still work but may miss some sentiment cues. You can add more keywords in the Sentiment Keyword List (Appendix C).
How are synonyms handled?
- The Pain‑Point Normalization Table (Appendix C) maps synonyms to a common term (e.g., “slow”, “sluggish”, “slow‑loading” → “Slow performance”).

Appendix B – Glossary

Term	Definition
Sentiment	The overall emotional tone (Positive, Negative, or Neutral) expressed in a review.
Positive Review	A review containing more positive words than negative words.
Negative Review	A review containing more negative words than positive words.
Neutral Review	A review with equal or no clear sentiment words.
Pain‑point	A specific problem or difficulty mentioned by a customer (e.g., “slow loading”).
Top Pain Points	The most frequently mentioned problems across a set of reviews.
Overall Sentiment	A single label summarizing the majority sentiment of the whole review set.
Sentiment Breakdown	A count and percentage of Positive, Negative, and Neutral reviews.
Normalization	The process of converting different wordings that refer to the same problem into a common term.

Appendix C – Reference Materials

C.1 Sentiment Keyword List

Positive Keywords (any occurrence counts as a positive cue)

amazing, awesome, excellent, great, good, fantastic, love, nice, wonderful, perfect, superb, wonderful, smooth, fast, quick, intuitive, easy, helpful, reliable, stable, intuitive, happy, satisfied, impressive, reliable, works, smooth, seamless, fast, responsive, excellent, perfect, love, great, amazing, fantastic.

Negative Keywords (any occurrence counts as a negative cue)

bad, terrible, awful, poor, slow, lag, lagging, delay, delayed, glitch, bug, error, failure, fail, crash, crashes, broken, confusing, difficult, hard, impossible, messy, problematic, frustrating, unsatisfied, unsatisfied, unsatisfying, terrible, awful, terrible, problematic, bug, issue, problem, flaw, mistake, complaint.

Neutral/None – Any word not listed above is ignored for sentiment scoring.

C.2 Pain‑Point Keyword List

Keyword / Phrase	Normalized Category
slow, sluggish, lag, latency, load time, loading time	Performance – Speed
crash, crashes, crashing, crash‑loop, freeze, frozen, unresponsive	Reliability – Stability
confusing, confusingly, unclear, hard to understand, not clear	Usability – Clarity
missing, absent, not there, unavailable, missing feature	Feature Gap
error, errors, bug, bugs, defect, defected, faulty	Quality – Bugs
heavy, bulky, large, unwieldy, large file	Performance – Size
expensive, pricey, cost, pricing, expensive, high cost	Cost – Pricing
not intuitive, unintuitive, difficult to use, hard to use	Usability – Ease of Use
slow, performance, speed, sluggish	Performance – Speed
unreliable, unstable, unpredictable	Reliability – Stability
difficult, hard, difficult to navigate, confusing navigation	Usability – Navigation
lack of, missing, no, absent, missing feature	Feature Gap
slow response, delayed response, latency	Performance – Speed
inaccurate, incorrect, wrong	Quality – Accuracy
cluttered, messy, disorganized, chaotic	Usability – Layout
slow, sluggish, laggy, latency	Performance – Speed
crash, crash, freeze, freezing	Reliability – Stability
slow, lag, slow load, slow load time	Performance – Speed
error, error message, error codes, error, bug, bug report	Quality – Bugs
confusing, confusingly, confusing UI, confusing navigation	Usability – Clarity

C.3 Pain‑Point Normalization Table

Variant Phrase	Normalized Term
“slow”, “sluggish”, “lag”, “latency”, “slow loading”, “load time”	Performance – Speed
“crash”, “crashes”, “crashing”, “freeze”, “frozen”, “unresponsive”	Reliability – Stability
“confusing”, “confusingly”, “unclear”, “hard to understand”, “not clear”	Usability – Clarity
“missing”, “absent”, “not there”, “unavailable”, “missing feature”, “lack of”	Feature Gap
“error”, “errors”, “bug”, “bugs”, “defect”, “fault”	Quality – Bugs
“heavy”, “bulky”, “large”, “big”, “large file”	Performance – Size
“expensive”, “pricey”, “cost”, “high cost”, “overpriced”	Cost – Pricing
“difficult”, “hard”, “hard to use”, “hard to understand”, “not intuitive”, “intuitive”	Usability – Ease of Use
“unreliable”, “unstable”, “unpredictable”	Reliability – Stability
“cluttered”, “messy”, “disorganized”, “chaotic”	Usability – Layout
“inaccurate”, “incorrect”, “wrong”	Quality – Accuracy

C.4 Example Sentiment & Pain‑Point Mapping

Example Review	Detected Sentiment	Extracted Pain‑Points (Normalized)
“The dashboard looks great but the page loads so slowly.”	Mixed (Positive words: “great”; Negative words: “slowly”)	Performance – Speed
“I love the new features, but the app crashes when I try to export.”	Mixed (Positive: “love”; Negative: “crashes”)	Reliability – Stability
“The app is fantastic, no issues at all.”	Positive	None

Additional Notes

Ensure that the Sentiment Keyword List and Pain‑Point Keyword List are kept up‑to‑date as product language and customer terminology evolve.
For best results, standardize the format of the review text before providing it to the process (e.g., remove HTML tags, convert smart quotes to standard quotes).

Input	Result
List of review IDs and plain‑text comments	Overall sentiment label, percentage breakdown, ranked top‑5 pain points, and a notes section that flags missing or unprocessed entries
(Optional) Additional keyword lists	Tailored sentiment and pain‑point detection that aligns with your product’s terminology

Review Sentiment Aggregator: Quick Insight From Customer Voices