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Movie Review Sentiment and Rationale Dataset
Entertainment & Media Consumption
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset was created to help researchers gain a deep understanding of human-generated movie reviews. It offers insights into aspects such as sentiment labels and the underlying rationales for these reviews. By analysing the information within, users can discover patterns and correlations that are invaluable for developing models capable of uncovering the importance of unique human perspectives in interpreting movie reviews. It aims to provide useful insights into better understanding user intent when reviewing movies.
Columns
- review: This column contains the actual text of the movie review. (String)
- label: This indicates the sentiment label assigned to the review, which can be Positive, Negative, or Neutral. It is represented by an integer (1 for Positive, -1 for Negative, 0 for Neutral). (Integer)
- evidence: This column provides evidence or rationales associated with the reviews, which can be used to validate or train models for understanding human perspectives.
Distribution
The dataset is provided in CSV format and includes distinct train, test, and validation sets. Specific numbers for rows or records are not explicitly available within the provided information. However, the 'evidences' column within the test set contains 199 unique values, and the 'label' column also has 199 unique values, indicating the scale of some of the contained data.
Usage
This dataset is ideal for various applications, including:
- Analysing human-generated movie reviews, their sentiments, and the rationales behind them.
- Developing advanced models to interpret human perspectives and user intent in movie reviews.
- Natural Language Processing (NLP) tasks and other Artificial Intelligence (AI) applications.
- Building an automated movie review summariser based on user ratings.
- Predicting review sentiment by combining machine learning models with human-annotated rationales.
- Creating AI systems to detect linguistic markers of deception in reviews.
- Developing simple machine learning recommendation systems.
To use the dataset, one needs a suitable working environment, such as Python or R, with access to NLP libraries. The recommended steps involve importing the CSV files, preprocessing text data in the 'review' and 'label' columns, training and testing machine learning algorithms using feature extraction techniques like Bag Of Words, TF-IDF, or Word2Vec, and then measuring performance accuracy.
Coverage
The dataset's regional scope is Global. No specific information regarding time range or demographic scope is detailed in the available sources.
License
CC0.
Who Can Use It
This dataset is particularly useful for:
- Data scientists seeking to explore and analyse movie review data.
- Researchers interested in AI applications, machine learning, and understanding human behaviour in online reviews.
- Developers looking to build or enhance systems related to sentiment analysis, recommendation, or text summarisation.
- Anyone aiming to gain insights into human perspectives when interpreting movie reviews.
Dataset Name Suggestions
- Movie Rationales (Rationales For Movie Reviews)
- Movie Review Sentiment Analysis
- Human Movie Review Rationales
- Movie Review Sentiment and Rationale Dataset
- User Intent in Movie Reviews
Attributes
Original Data Source: Movie Rationales (Rationales For Movie Reviews)
QUALITY