# 🧠 Review Sentiment Analyzer — Themes, Trends & Complaints (`nexgenwatch/review-sentiment-analyzer`) Actor

Turn any review set into themes, complaints, sentiment % and trends.

- **URL**: https://apify.com/nexgenwatch/review-sentiment-analyzer.md
- **Developed by:** [NexGen Watch](https://apify.com/nexgenwatch) (community)
- **Categories:** Business, Agents
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $335.00 / 1,000 100 reviews analyzeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## 🧠 Review Sentiment Analyzer — Themes, Trends & Complaints

Extractors stop at raw reviews. Product, CX, and reputation teams pay for the *analysis* — themes, complaint clusters, sentiment percentages, and trends they can act on this week.

### What you get

- Themes and bigram topics ranked by mention count
- Complaint clusters that group the recurring pain points
- Sentiment split (positive / neutral / negative %) plus counts and an average sentiment score
- Time-bucketed trend data and a stable analysis ID for like-for-like delta comparisons later
- Developer-response coverage %, representative example reviews, and run metadata

### Use cases by team

- **Product:** See which features drive praise vs. churn without reading thousands of reviews by hand.
- **Customer experience:** Surface the top complaint clusters for this release and track whether they shrink next release.
- **Reputation / brand:** Quantify sentiment movement over time with reproducible numbers you can put in a report.
- **Analysts:** Get auditable, deterministic scores that don't drift run-to-run — safe for regulated or repeatable reporting.

### Example inputs

*Analyze inline reviews*

```json
{
  "reviews": [
    {
      "review_text": "Great and fast",
      "rating": 5
    },
    {
      "review_text": "Crashes after login",
      "rating": 1
    }
  ]
}
```

*Analyze an Apify dataset you already collected*

```json
{
  "dataset_id": "YOUR_DATASET_ID",
  "max_items": 5000
}
```

*Tune theme extraction*

```json
{
  "reviews": [
    {
      "review_text": "Battery drains overnight",
      "rating": 2
    }
  ],
  "max_themes": 20
}
```

### 📊 Sample Output

```json
{
  "review_count": 2,
  "sentiment_positive_pct": 50.0,
  "sentiment_negative_pct": 50.0,
  "sentiment_counts": {
    "positive": 1,
    "neutral": 0,
    "negative": 1
  },
  "average_rating": 3.0,
  "themes": [
    {
      "theme": "crashes login",
      "mentions": 1
    }
  ],
  "complaint_clusters": [
    {
      "cluster": "stability",
      "mentions": 1
    }
  ],
  "method": "ngd-lexicon-v1",
  "partial": false
}
```

### How it works

Feed it inline reviews or a user-authorized Apify dataset ID. It scores every review with a transparent deterministic lexicon-plus-rating-prior model (`ngd-lexicon-v1`) — **not an LLM**. The same reviews in produce the same analysis out, every time: no model drift, no API key, no per-token fees. Every score is reproducible and auditable. No external review source is fetched — you bring the data, it does the analysis.

### Pricing

| Event | Price (FREE tier) |
|---|---|
| Actor start (`apify-actor-start`) | $0.05 flat |
| Reviews analyzed (`reviews-analyzed-100`) | $0.50 per 100 reviews, block-rounded |

Pay-per-event with a four-tier ladder (FREE → Gold): $0.50 / $0.45 / $0.40 / $0.335 per 100 reviews. Apify applies its automatic plan discount on top. Worked example: 250 reviews on FREE = $0.05 start + 3 × $0.50 = **$1.55**. Blocked or zero-result runs do not intentionally charge. New to Apify? [Start free](https://apify.com/fpr=2ayu9b).

### Run it — API, CLI, MCP

API:

```bash
curl -X POST "https://api.apify.com/v2/acts/nexgenwatch~review-sentiment-analyzer/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" -d @input.json
```

CLI:

```bash
apify call nexgenwatch/review-sentiment-analyzer -i @input.json
```

MCP: expose `nexgenwatch/review-sentiment-analyzer` through Apify's MCP server, select it as a tool, and pass the same input object.

### Related actors — App Intelligence cluster

| Actor | What it does |
|---|---|
| [Apple App Store Reviews](https://apify.com/nexgenwatch/ios-app-reviews?fpr=2ayu9b) | Scrape public Apple App Store reviews for any app. |
| [Google Play Reviews](https://apify.com/nexgenwatch/google-play-reviews?fpr=2ayu9b) | Scrape public Google Play reviews for any app. |
| [App Metadata Scraper](https://apify.com/nexgenwatch/app-store-metadata?fpr=2ayu9b) | Pull app metadata from Apple & Google Play in one schema. |
| [App Rankings Tracker](https://apify.com/nexgenwatch/app-rankings-tracker?fpr=2ayu9b) | Track Apple App Store top-chart rankings by country. |
| [ASO Keyword Tracker](https://apify.com/nexgenwatch/aso-keyword-tracker?fpr=2ayu9b) | Track keyword rankings across App Store & Google Play. |

### Compliance & honest limits

- You supply the review data (inline or an Apify dataset you authorize); this actor does not scrape a review source itself.
- Deterministic lexicon model — powerful for reproducibility, but it is not a generative summarizer and will not write freeform narratives.
- A source-confirmed empty input returns `SUCCEEDED-0: GENUINE_EMPTY`; truncated runs are labeled `PARTIAL`.

### FAQ

**Is this AI or an LLM?**

No — deliberately. It is a deterministic lexicon-and-rating-prior model (`ngd-lexicon-v1`): same reviews in, same analysis out, with no model drift and no API key. That reproducibility is the point — it suits regulated, comparable, or repeatable reporting where an LLM's run-to-run variation is a liability.

**Where do the reviews come from?**

You provide them — paste them inline or pass a `dataset_id` from a review scraper (for example our Apple App Store or Google Play reviews actors). Nothing external is fetched.

**How does pricing work?**

Pay-per-event: a $0.05 start plus $0.50 per 100 reviews analyzed on FREE (lower at Bronze / Silver / Gold). All-in — no separate LLM or API-key cost.

**Does zero mean blocked?**

No. A confirmed-empty input is `SUCCEEDED-0: GENUINE_EMPTY`; only exhausted retries fail, and truncated runs are labeled `PARTIAL`.

**Can I compare two batches over time?**

Yes. Each run returns a stable analysis ID and deterministic period buckets so a later delta layer can compare like-for-like batches.

**What languages does it handle?**

The lexicon is tuned for English review text; other languages score but with reduced theme quality.

**Can I control how many themes come back?**

Yes — set `max_themes` (default 12, up to 50).

### Troubleshooting

- **Empty output?** Confirm your `reviews` array is non-empty or that `dataset_id` points to a dataset you own; a genuinely empty source returns GENUINE\_EMPTY by design.
- **Dataset not read?** The dataset ID must belong to your account and contain review-shaped objects; check `max_items` isn't set below your row count.
- **Themes look thin?** Raise `max_themes`, and make sure `review_text` (not just `rating`) is populated.
- **Numbers differ from last run?** They shouldn't — identical input yields identical output. If they differ, your input changed.
- **Need sentiment-shift alerts?** That delta layer is Phase 2; today's buckets + analysis ID are the foundation for it.

### About

Part of the NexGenData App Intelligence cluster — a closed web of review, metadata, ranking, and keyword actors that share one honest, deterministic contract. Built by NexGenData. Questions or need a custom feed? Open an issue on the Actor page.

# Actor input Schema

## `dataset_id` (type: `string`):

Optional user-authorized Apify dataset ID containing reviews.

## `reviews` (type: `array`):

Buyer-supplied review objects; used when dataset\_id is empty.

## `max_items` (type: `integer`):

Hard cap on records delivered by this run.

## `max_themes` (type: `integer`):

Maximum theme terms or bigrams returned.

## `proxy_configuration` (type: `object`):

Optional Apify proxy settings; a fresh session is requested for every retry.

## Actor input object example

```json
{
  "dataset_id": "",
  "reviews": [
    {
      "review_text": "Great and fast",
      "rating": 5
    },
    {
      "review_text": "Crashes after login",
      "rating": 1
    }
  ],
  "max_items": 5000,
  "max_themes": 12,
  "proxy_configuration": {
    "useApifyProxy": true
  }
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("nexgenwatch/review-sentiment-analyzer").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("nexgenwatch/review-sentiment-analyzer").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call nexgenwatch/review-sentiment-analyzer --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=nexgenwatch/review-sentiment-analyzer",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/dtgB5fCYzSDj5kyZ8/builds/VtFriaqcEaowTdxn5/openapi.json