# AutoTrain Dataset for project: sentiment_analysis

## Dataset Description

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

### 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
[
  {
    "text": "I grew up (b. 1965) watching and loving the Thunderbirds. All my mates at school watched. We played \"Thunderbirds\" before school, during lunch and after school. We all wanted to be Virgil or Scott. No one wanted to be Alan. Counting down from 5 became an art form. I took my children to see the movie hoping they would get a glimpse of what I loved as a child. How bitterly disappointing. The only high point was the snappy theme tune. Not that it could compare with the original score of the Thunderbirds. Thankfully early Saturday mornings one television channel still plays reruns of the series Gerry Anderson and his wife created. Jonatha Frakes should hand in his directors chair, his version was completely hopeless. A waste of film. Utter rubbish. A CGI remake may be acceptable but replacing marionettes with Homo sapiens subsp. sapiens was a huge error of judgment.",
    "target": 0
  },
  {
    "text": "When I put this movie in my DVD player, and sat down with a coke and some chips, I had some expectations. I was hoping that this movie would contain some of the strong-points of the first movie: Awsome animation, good flowing story, excellent voice cast, funny comedy and a kick-ass soundtrack. But, to my disappointment, not any of this is to be found in Atlantis: Milo's Return. Had I read some reviews first, I might not have been so let down. The following paragraph will be directed to those who have seen the first movie, and who enjoyed it primarily for the points mentioned.<br /><br />When the first scene appears, your in for a shock if you just picked Atlantis: Milo's Return from the display-case at your local videoshop (or whatever), and had the expectations I had. The music feels as a bad imitation of the first movie, and the voice cast has been replaced by a not so fitting one. (With the exception of a few characters, like the voice of Sweet). The actual drawings isnt that bad, but the animation in particular is a sad sight. The storyline is also pretty weak, as its more like three episodes of Schooby-Doo than the single adventurous story we got the last time. But dont misunderstand, it's not very good Schooby-Doo episodes. I didnt laugh a single time, although I might have sniggered once or twice.<br /><br />To the audience who haven't seen the first movie, or don't especially care for a similar sequel, here is a fast review of this movie as a stand-alone product: If you liked schooby-doo, you might like this movie. If you didn't, you could still enjoy this movie if you have nothing else to do. And I suspect it might be a good kids movie, but I wouldn't know. It might have been better if Milo's Return had been a three-episode series on a cartoon channel, or on breakfast TV.",
    "target": 0
  }
]
```

### Dataset Fields

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

```json
{
  "text": "Value(dtype='string', id=None)",
  "target": "ClassLabel(names=['0', '1'], 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        | 1497 |
| valid        | 1497 |

task_categories:text-classification language:en region:us
{"language": ["en"], "task_categories": ["text-classification"]}
TAGS
#task_categories-text-classification #language-English #region-us 

AutoTrain Dataset for project: sentiment\_analysis
==================================================


Dataset Description
-------------------


This dataset has been automatically processed by AutoTrain for project sentiment\_analysis.


### Languages


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


Dataset Structure
-----------------


### Data Instances


A sample from this dataset looks as follows:


### Dataset Fields


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


### Dataset Splits


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




### Languages


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


Dataset Structure
----------------- ### Data Instances


A sample from this dataset looks as follows: ### Dataset Fields


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


This dataset is split into a train and validation split. The split sizes are as follow:
TAGS
#task_categories-text-classification #language-English #region-us 
 ### Languages


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


Dataset Structure
----------------- ### Data Instances


A sample from this dataset looks as follows: ### Dataset Fields


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


This dataset is split into a train and validation split. The split sizes are as follow:
passage: TAGS
#task_categories-text-classification #language-English #region-us 
### Languages


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


Dataset Structure
-----------------### Data Instances


A sample from this dataset looks as follows:### Dataset Fields


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


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