# Employee Sentiment Analysis Dataset from Indeed

**Use case:** 

Four hundred written reviews of one fast-food chain: the full text, headline, pros and cons as the input, and the overall rating plus five sub-ratings as the labels, so a model can be trained per category - management, pay, work-life balance - rather than on one blended score. Job title and current-or-former status slice the corpus. Cost: 400 review rows at $0.002 = at most $0.80 a run.

## Input

```json
{
  "companies": [
    "McDonald's"
  ],
  "countries": [
    "us"
  ],
  "maxReviewsPerCompany": 400,
  "sort": "newest",
  "minRating": 0,
  "maxRating": 0,
  "requireText": true,
  "jobTitleContains": "",
  "locationContains": "",
  "sinceDate": "",
  "includeCompanyRow": true,
  "sessions": 2,
  "perIp": 1,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}
```

## Output

```json
{
  "company_name": {
    "label": "Company name",
    "format": "string"
  },
  "review_id": {
    "label": "Review id",
    "format": "string"
  },
  "rating": {
    "label": "Rating",
    "format": "number"
  },
  "title": {
    "label": "Title",
    "format": "string"
  },
  "text": {
    "label": "Text",
    "format": "string"
  },
  "pros": {
    "label": "Pros",
    "format": "string"
  },
  "cons": {
    "label": "Cons",
    "format": "string"
  },
  "job_title": {
    "label": "Job title",
    "format": "string"
  },
  "location": {
    "label": "Location",
    "format": "string"
  },
  "employment_status": {
    "label": "Employment status",
    "format": "string"
  },
  "review_date": {
    "label": "Review date",
    "format": "string"
  },
  "work_life_balance": {
    "label": "Work life balance",
    "format": "integer"
  },
  "compensation_benefits": {
    "label": "Compensation benefits",
    "format": "integer"
  },
  "job_security_advancement": {
    "label": "Job security advancement",
    "format": "integer"
  },
  "management": {
    "label": "Management",
    "format": "integer"
  },
  "culture_values": {
    "label": "Culture values",
    "format": "integer"
  },
  "helpful_count": {
    "label": "Helpful count",
    "format": "integer"
  },
  "employer_reply": {
    "label": "Employer reply",
    "format": "string"
  },
  "url": {
    "label": "Url",
    "format": "string"
  }
}
```

## About this Actor

This example demonstrates how to use [Indeed Company Reviews Scraper — Employee Reviews](https://apify.com/kestrel/indeed-company-reviews.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/kestrel/indeed-company-reviews.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/kestrel/indeed-company-reviews.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).