task_categories:summarization language:en region:us
---
language:
- en
task_categories:
- summarization

---
# AutoTrain Dataset for project: rwlv_summarizer

## Dataset Description

This dataset has been automatically processed by AutoTrain for project rwlv_summarizer.

### Languages

The BCP-47 code for the dataset's language is en.

## Dataset Structure

### Data Instances

A sample from this dataset looks as follows:

```json
[
  {
    "feat_platform": "Yelp",
    "feat_line_of_business": "RWLV",
    "text": "I decided to come to Resorts World to grab some sushi on a Sunday afternoon. I was so glad to see the trash gone from the parking garage. The grounds outside the building were so much nicer than my first visit. Planters were finished and the place was clean. It looked good. All the employees that I encountered were just as nice and helpful as my first visit. Bathrooms were clean. Food was great! My only complaint is that I couldn't believe how hard it was to gamble 73 cents left on my ticket! I mean they really stick it to you here. Some of the machines minimum bets were some crazy friggin number like 78 cents. Oh well. Get those pennies Resorts World. I will be back to try more food and maybe next time I'll stick with the tables. Come see Vegas newest Casino if you can.",
    "feat_reactions": 0.0,
    "feat_ratings": 4,
    "feat_sentiment_pys": "POS",
    "feat_sentiment_vad": "POS",
    "feat_sentiment_tb": "POS",
    "feat_sentiment_rat": "POS",
    "feat_sentiment_gpt": "POS",
    "feat_contextual": "facilities",
    "feat_intention": "compliment",
    "feat_intention_refined": "compliment",
    "feat_refined_gpt": "POS",
    "target": "positive review of resorts world with improved parking and grounds, friendly",
    "feat_emotion": "others"
  },
  {
    "feat_platform": "Yelp",
    "feat_line_of_business": "RWLV",
    "text": "The check-in line is extremely long and at the Hilton they seem understaffed. We went to the pool today. Granted it is 103\u00b0 outside however the pool is freezing. There is such thing as too cold. I did however get a Coca-Cola for nine dollars. Yes nine dollars for one can of Coke.",
    "feat_reactions": 7.0,
    "feat_ratings": 2,
    "feat_sentiment_pys": "NEU",
    "feat_sentiment_vad": "POS",
    "feat_sentiment_tb": "NEG",
    "feat_sentiment_rat": "NEG",
    "feat_sentiment_gpt": "NEG",
    "feat_contextual": "price",
    "feat_intention": "complaint",
    "feat_intention_refined": "complaint",
    "feat_refined_gpt": "NEG",
    "target": "long check-in, understaffed, freezing pool, expensive",
    "feat_emotion": "others"
  }
]
```

### Dataset Fields

The dataset has the following fields (also called "features"):

```json
{
  "feat_platform": "Value(dtype='string', id=None)",
  "feat_line_of_business": "Value(dtype='string', id=None)",
  "text": "Value(dtype='string', id=None)",
  "feat_reactions": "Value(dtype='float64', id=None)",
  "feat_ratings": "Value(dtype='int64', id=None)",
  "feat_sentiment_pys": "Value(dtype='string', id=None)",
  "feat_sentiment_vad": "Value(dtype='string', id=None)",
  "feat_sentiment_tb": "Value(dtype='string', id=None)",
  "feat_sentiment_rat": "Value(dtype='string', id=None)",
  "feat_sentiment_gpt": "Value(dtype='string', id=None)",
  "feat_contextual": "Value(dtype='string', id=None)",
  "feat_intention": "Value(dtype='string', id=None)",
  "feat_intention_refined": "Value(dtype='string', id=None)",
  "feat_refined_gpt": "Value(dtype='string', id=None)",
  "target": "Value(dtype='string', id=None)",
  "feat_emotion": "Value(dtype='string', id=None)"
}
```

### Dataset Splits

This dataset is split into a train and validation split. The split sizes are as follow:

| Split name   | Num samples         |
| ------------ | ------------------- |
| train        | 1539 |
| valid        | 385 |
