Lowerated/lm6-movies-reviews-aspects

--- license: apache-2.0 task_categories: - text-classification language: - en tags: - art - movies pretty_name: IMDB Reviews (Aspect based Formatted) size_categories: - 100K<n<1M --- # IMDB Reviews (Aspect Based Formatted) ## Overview **IMDB Reviews (Aspect Based Formatted)** is a specialized dataset designed for text classification tasks that involve identifying and categorizing specific aspects of movie reviews. The dataset focuses on extracting and labeling various elements of filmmaking, such as cinematography, story, characters, direction, and unique concepts, from user reviews on IMDB. The dataset can be used to develop and train models for aspect-based sentiment analysis, providing insights into how different aspects of a movie are perceived by audiences. This structured approach allows for more granular sentiment analysis and understanding of reviews, enhancing applications like movie recommendation systems, sentiment analysis tools, and more. ## License This dataset is released under the Apache 2.0 License, allowing for both commercial and non-commercial use. For full details, please refer to the [license file](LICENSE). ## Task Categories - **Text Classification**: The primary task for this dataset is classifying text into predefined categories, specifically focusing on various aspects of movie reviews. - **Aspect-Based Sentiment Analysis**: Another critical task is analyzing sentiments related to specific aspects of filmmaking to understand audience perceptions better. ## Language - **English (en)**: The dataset consists of reviews written in English, making it suitable for English language text processing and analysis tasks. ## Tags - **Art**: The dataset provides insights into artistic elements of films, such as cinematography and direction. - **Movies**: Focuses on movie reviews, analyzing various aspects of filmmaking and audience sentiment. ## Pretty Name **IMDB Reviews (Aspect Based Formatted)** ## Size Categories - **100K < n < 1M**: The dataset contains a substantial number of entries, making it suitable for training and evaluation of machine learning models that require a significant amount of data. ## Dataset Structure The dataset is structured to provide comprehensive information about movie reviews and the aspects they focus on. Here's a breakdown of the main columns: - **Review**: The complete text of the movie review, providing context and content for analysis. - **Aspect**: The specific aspect of filmmaking that the review addresses, such as Story, Characters, or Unique Concept. - **Snippets**: Key phrases or sentences extracted from the review that relate specifically to the identified aspect. - **Aspect Encoded**: A numerical encoding representing the aspect for easy use in machine learning models. ### Example Entry | Review ID | Review Snippet | Aspect | Snippets | Aspect Encoded | |-----------|--------------------------------------------------------------------------------|----------------|-----------------------------------------------|----------------| | 0 | The cinematography was stunning, but the story was weak. | Story | [The story was weak] | 5 | | 1 | I loved the movie. There wasn't anything unique in the movie. | Unique Concept | [There wasn't anything unique in the movie] | 6 | | 2 | Characters could've been better tho. | Characters | [Characters could've been better tho] | 0 | ## Dataset Features 1. **Aspect Diversity**: Includes a wide range of aspects related to filmmaking, providing a holistic view of movie reviews. 2. **Aspect-Specific Sentiments**: Enables analysis of sentiments specific to each aspect, improving understanding of audience perceptions. 3. **Structured Format**: Organized in a structured format that facilitates easy integration with text classification models. ## Use Cases - **Aspect-Based Sentiment Analysis**: Train models to analyze sentiments related to specific movie aspects. - **Recommendation Systems**: Enhance movie recommendation algorithms by considering detailed opinions on different aspects. - **Market Research**: Understand audience preferences and perceptions to inform filmmaking and marketing strategies. ## Contribution We welcome contributions to this dataset in the form of additional reviews, aspect annotations, or improvements to existing entries. Please refer to our contribution guidelines for more information. ## Citation If you use this dataset in your research or application, please cite it as follows: ``` @dataset{lowerated_lm6_imdb_reviews_aspects_2024, author = {Muhammad Wisal}, title = {IMDB Reviews (Aspect Based Formatted)}, year = {2024}, url = {https://github.com/Lowerated/lm6-movies-reviews-aspects} } ``` ## Acknowledgments We extend our gratitude to the contributors and the Lowerated team for their efforts in creating and maintaining this dataset.--- license: Apache-2.0 任务类别: - 文本分类 语言: - 英语 标签: - 艺术 - 电影 规范名称: IMDB影评（基于方面格式化版） 规模类别: - 100K<n<1M --- # IMDB影评（基于方面格式化版） ## 概览 **IMDB影评（基于方面格式化版）** 是专为面向电影影评特定方面识别与分类的文本分类任务设计的专业数据集。该数据集聚焦于从IMDB用户影评中提取并标注电影制作的各类元素，例如摄影、剧情、角色、导演手法以及独特创意等。 本数据集可用于开发和训练面向方面的情感分析（Aspect-Based Sentiment Analysis）模型，帮助洞察观众对电影不同方面的看法。这种结构化的处理方式支持更精细化的情感分析与影评理解，可优化电影推荐系统、情感分析工具等各类应用场景。 ## 许可证 本数据集采用Apache 2.0许可证发布，允许商业与非商业使用。完整细节请参阅[许可证文件](LICENSE)。 ## 任务类别 - **文本分类**：本数据集的核心任务是将文本归类至预定义类别，重点聚焦电影影评的各类特定方面。 - **面向方面的情感分析**：另一项关键任务是分析与电影制作特定方面相关的情感倾向，以更深入地理解观众的看法。 ## 语言 - **英语（en）**：本数据集收录的影评均为英语，适用于英语语言文本处理与分析任务。 ## 标签 - **艺术**：本数据集涵盖电影艺术元素的相关分析，例如摄影与导演手法。 - **电影**：聚焦电影影评，分析电影制作的各类方面与观众情感倾向。 ## 规范名称 **IMDB影评（基于方面格式化版）** ## 规模类别 - **100K < n < 1M**：本数据集包含大量样本，适用于需要大量数据的机器学习模型的训练与评估。 ## 数据集结构 本数据集的结构旨在提供电影影评及其聚焦方面的全面信息。以下为主要列项说明： - **Review（影评原文）**：电影影评的完整文本，为分析提供上下文与内容支撑。 - **Aspect（分析方面）**：影评所涉及的电影制作特定方面，例如剧情、角色或独特创意。 - **Snippets（相关片段）**：从影评中提取的、与已识别方面直接相关的关键短语或句子。 - **Aspect Encoded（方面编码）**：用于机器学习模型便捷使用的方面数值编码。 ### 示例条目 | 影评ID | 影评片段 | 分析方面 | 相关片段 | 方面编码 | |--------|----------|----------|----------|----------| | 0 | 摄影堪称惊艳，但剧情略显薄弱。 | 剧情 | ["剧情略显薄弱"] | 5 | | 1 | 我很喜欢这部电影，但并无特别新颖之处。 | 独特创意 | ["并无特别新颖之处"] | 6 | | 2 | 角色塑造本可以更出色。 | 角色 | ["角色塑造本可以更出色"] | 0 | ## 数据集特性 1. **方面多样性**：涵盖与电影制作相关的广泛方面，可全面展现影评内容。 2. **面向方面的情感分析**：支持针对每个特定方面的情感分析，提升对观众看法的理解深度。 3. **结构化格式**：采用结构化格式组织，便于与文本分类模型快速集成。 ## 应用场景 - **面向方面的情感分析**：训练模型以分析与电影特定方面相关的情感倾向。 - **推荐系统**：通过考量用户对电影不同方面的详细评价，优化电影推荐算法。 - **市场调研**：洞察观众偏好与看法，为电影制作与营销策略提供参考。 ## 贡献 我们欢迎以新增影评、方面标注或优化现有条目等形式为该数据集作出贡献。详情请参阅我们的贡献指南。 ## 引用 若您在研究或应用中使用本数据集，请按以下格式引用： @dataset{lowerated_lm6_imdb_reviews_aspects_2024, author = {Muhammad Wisal}, title = {IMDB Reviews (Aspect Based Formatted)}, year = {2024}, url = {https://github.com/Lowerated/lm6-movies-reviews-aspects} } ## 致谢 我们感谢所有贡献者以及Lowerated团队为创建和维护本数据集所付出的努力。