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Harry Potter Reviews Dataset

A comprehensive analysis of 491 reviews of the famous "Harry Potter and the Sorcerer's Stone," which can be used for sentiment analysis, marketing and sales analytics, and other data-driven applications.

    • Labeling Type: Rating
    • Data Format: Text
    • Data Type: Synthetic Data

Main product

Data Quantity (Samples)

Total Price

$ 2,500

(VAT Included)

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About Dataset

1) Data introduction

• Harry-potter-reviews dataset contains 491 comprehensive reviews of the famous “Harry Potter and the Sorcerer’s Stone.”

2) Data utilization

(1)Harry-potter-reviews data has characteristics that: • Contains 491 comprehensive reviews of the film. The reviews were generated using Large Language Models (LLMs). (2) Harry-potter-reviews data can be used to: • Sentiment analysis: This dataset can be used to train a model that measures the overall reader satisfaction by analyzing the sentiment of reviews and classifying them as positive, negative, or neutral. • Marketing and sales analytics: Publishers and marketers can use insights from data to understand reader preferences and improve marketing strategies for similar books.

Meta Data

DomainConsumerZoodata formatsText
Zoodata volume1000 itemsRegistration date2025.02.26
Zoodata typeSynthetic dataExistence of labelingExist
Labeling typeRatingLabeling formatsjson

Good

Performance 1
85

Outstanding

Performance 2
100

Data Samples

Sample Data

Utility

Downstream Classification (▲)MMD (▼)One Class Classification (▼)
Total000
SuitabilityOKOKOK

The higher the value, the better (▲)

Model Performance

Downstream classification accuracy is an indicator used to evaluate the usefulness of synthetic data. It measures whether synthetic data performs similarly to real data. The method involves training the same model separately on real data and synthetic data, and then comparing the accuracies of the two models. Interpretation: A high accuracy rate means that the model trained on synthetic data performs similarly to the one trained on real data, indicating that the synthetic data is of high quality and well represents the real data.

The closer to zero or the lower the value, the better (▼)

Quality

MMD (Maximum Mean Discrepancy) is a metric used to assess the similarity between two probability distributions. It is commonly used to compare generated data with real data. High MMD score: A score above 0.05 indicates that the two distributions may differ. Low MMD score: Indicates that the generated data is similar to the real data. A score close to 0 is preferable, and a score below 0.01 suggests that the two data distributions are nearly indistinguishable.


Privacy

ROUGE (▼)BERTScore (▼)
Total00
SuitabilityOKOK

The closer to zero or the lower the value, the better (▼)

Structural Similarity

Inference risk measures the risk of inferring sensitive information about the original data from synthetic data. It evaluates the likelihood of extracting original data information from synthetic data and is calculated by comparing the distance between synthetic and original data. High Duplication Rate: Indicates lower data diversity and potential quality issues, which can reduce the reliability of analysis and models. Interpretation: A lower risk value means that synthetic data is less likely to infer sensitive information, indicating higher data security.

Perceptual Similarity

BERTScore is a metric used to evaluate the semantic similarity between two texts, utilizing deep learning models like BERT (Bidirectional Encoder Representations from Transformers) to capture the meaning of the texts.. High BERTScore: If the value is 0.9 or higher, it indicates that the generated text is almost semantically identical to the real text. This can increase the risk of sensitive information leakage. Low BERTScore: If the value is 0.6 or lower, it indicates lower similarity and a reduced risk of leakage.

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