
# Dataset Card for amazon reviews for sentiment analysis

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://kaggle.com/datasets/tarkkaanko/amazon
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

One of the most important problems in e-commerce is the correct calculation of the points given to after-sales products. The solution to this problem is to provide greater customer satisfaction for the e-commerce site, product prominence for sellers, and a seamless shopping experience for buyers. Another problem is the correct ordering of the comments given to the products. The prominence of misleading comments will cause both financial losses and customer losses. In solving these 2 basic problems, e-commerce site and sellers will increase their sales, while customers will complete their purchasing journey without any problems. 

This dataset consists of ranking product ratings and reviews on Amazon. Please review this notebook to observe how I came up with this [dataset](https://www.kaggle.com/code/tarkkaanko/rating-product-sorting-reviews-in-amazon) This dataset containing Amazon Product Data includes product categories and various metadata. 

----

### What is expected of you?

The product with the most comments in the electronics category has user ratings and comments. In this way, we expect you to perform sentiment analysis with your specific methods.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

[More Information Needed]

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

### Data Splits

[More Information Needed]

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

This dataset was shared by [@tarkkaanko](https://kaggle.com/tarkkaanko)

### Licensing Information

The license for this dataset is cc-by-nc-sa-4.0

### Citation Information

```bibtex
[More Information Needed]
```

### Contributions

[More Information Needed]
license:cc-by-nc-sa-4.0 region:us
{"license": ["cc-by-nc-sa-4.0"], "kaggle_id": "tarkkaanko/amazon"}
TAGS
#license-cc-by-nc-sa-4.0 #region-us 


# Dataset Card for amazon reviews for sentiment analysis

## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions

## Dataset Description

- Homepage: URL
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:

### Dataset Summary

One of the most important problems in e-commerce is the correct calculation of the points given to after-sales products. The solution to this problem is to provide greater customer satisfaction for the e-commerce site, product prominence for sellers, and a seamless shopping experience for buyers. Another problem is the correct ordering of the comments given to the products. The prominence of misleading comments will cause both financial losses and customer losses. In solving these 2 basic problems, e-commerce site and sellers will increase their sales, while customers will complete their purchasing journey without any problems. 

This dataset consists of ranking product ratings and reviews on Amazon. Please review this notebook to observe how I came up with this dataset This dataset containing Amazon Product Data includes product categories and various metadata. 

----

### What is expected of you?

The product with the most comments in the electronics category has user ratings and comments. In this way, we expect you to perform sentiment analysis with your specific methods.

### Supported Tasks and Leaderboards



### Languages



## Dataset Structure

### Data Instances



### Data Fields



### Data Splits



## Dataset Creation

### Curation Rationale



### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators

This dataset was shared by @tarkkaanko

### Licensing Information

The license for this dataset is cc-by-nc-sa-4.0





### Contributions


# Dataset Card for amazon reviews for sentiment analysis ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: URL
- Repository:
- Paper:
- Leaderboard:
- Point of Contact: ### Dataset Summary

One of the most important problems in e-commerce is the correct calculation of the points given to after-sales products. The solution to this problem is to provide greater customer satisfaction for the e-commerce site, product prominence for sellers, and a seamless shopping experience for buyers. Another problem is the correct ordering of the comments given to the products. The prominence of misleading comments will cause both financial losses and customer losses. In solving these 2 basic problems, e-commerce site and sellers will increase their sales, while customers will complete their purchasing journey without any problems. 

This dataset consists of ranking product ratings and reviews on Amazon. Please review this notebook to observe how I came up with this dataset This dataset containing Amazon Product Data includes product categories and various metadata. 

---- ### What is expected of you?

The product with the most comments in the electronics category has user ratings and comments. In this way, we expect you to perform sentiment analysis with your specific methods. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators

This dataset was shared by @tarkkaanko ### Licensing Information

The license for this dataset is cc-by-nc-sa-4.0 ### Contributions
TAGS
#license-cc-by-nc-sa-4.0 #region-us 
 # Dataset Card for amazon reviews for sentiment analysis ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: URL
- Repository:
- Paper:
- Leaderboard:
- Point of Contact: ### Dataset Summary

One of the most important problems in e-commerce is the correct calculation of the points given to after-sales products. The solution to this problem is to provide greater customer satisfaction for the e-commerce site, product prominence for sellers, and a seamless shopping experience for buyers. Another problem is the correct ordering of the comments given to the products. The prominence of misleading comments will cause both financial losses and customer losses. In solving these 2 basic problems, e-commerce site and sellers will increase their sales, while customers will complete their purchasing journey without any problems. 

This dataset consists of ranking product ratings and reviews on Amazon. Please review this notebook to observe how I came up with this dataset This dataset containing Amazon Product Data includes product categories and various metadata. 

---- ### What is expected of you?

The product with the most comments in the electronics category has user ratings and comments. In this way, we expect you to perform sentiment analysis with your specific methods. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators

This dataset was shared by @tarkkaanko ### Licensing Information

The license for this dataset is cc-by-nc-sa-4.0 ### Contributions
passage: TAGS
#license-cc-by-nc-sa-4.0 #region-us 
# Dataset Card for amazon reviews for sentiment analysis## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions## Dataset Description

- Homepage: URL
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:### Dataset Summary

One of the most important problems in e-commerce is the correct calculation of the points given to after-sales products. The solution to this problem is to provide greater customer satisfaction for the e-commerce site, product prominence for sellers, and a seamless shopping experience for buyers. Another problem is the correct ordering of the comments given to the products. The prominence of misleading comments will cause both financial losses and customer losses. In solving these 2 basic problems, e-commerce site and sellers will increase their sales, while customers will complete their purchasing journey without any problems. 

This dataset consists of ranking product ratings and reviews on Amazon. Please review this notebook to observe how I came up with this dataset This dataset containing Amazon Product Data includes product categories and various metadata. 

----### What is expected of you?

The product with the most comments in the electronics category has user ratings and comments. In this way, we expect you to perform sentiment analysis with your specific methods.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information