Elibethpm25/analisis-sentimientos-imdb

---
size_categories: 10K<n<100K
tags:
- rlfh
- argilla
- human-feedback
---

# Dataset Card for analisis-sentimientos-imdb

This dataset has been created with [Argilla](https://github.com/argilla-io/argilla). As shown in the sections below, this dataset can be loaded into your Argilla server as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).

## Using this dataset with Argilla

To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:

```python
import argilla as rg

ds = rg.Dataset.from_hub("Elibethpm25/analisis-sentimientos-imdb", settings="auto")
```

This will load the settings and records from the dataset repository and push them to you Argilla server for exploration and annotation.

## Using this dataset with `datasets`

To load the records of this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:

```python
from datasets import load_dataset

ds = load_dataset("Elibethpm25/analisis-sentimientos-imdb")
```

This will only load the records of the dataset, but not the Argilla settings.

## Dataset Structure

This dataset repo contains:

* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `rg.Dataset.from_hub` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
* A dataset configuration folder conforming to the Argilla dataset format in `.argilla`.

The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.

### Fields

The **fields** are the features or text of a dataset's records. For example, the 'text' column of a text classification dataset of the 'prompt' column of an instruction following dataset.

| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| text | text | text | False | False |

### Questions

The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.

| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| label_0 | label_0 | label_selection | True | N/A | ['positive', 'negative'] |

<!-- check length of metadata properties -->

### Data Instances

An example of a dataset instance in Argilla looks as follows:

```json
{
"_server_id": "56fdf0c5-5720-408f-99f1-ae7b89d46242",
"fields": {
"text": "I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first it was seized by U.S. customs if it ever tried to enter this country, therefore being a fan of films considered \"controversial\" I really had to see this for myself.\u003cbr /\u003e\u003cbr /\u003eThe plot is centered around a young Swedish drama student named Lena who wants to learn everything she can about life. In particular she wants to focus her attentions to making some sort of documentary on what the average Swede thought about certain political issues such as the Vietnam War and race issues in the United States. In between asking politicians and ordinary denizens of Stockholm about their opinions on politics, she has sex with her drama teacher, classmates, and married men.\u003cbr /\u003e\u003cbr /\u003eWhat kills me about I AM CURIOUS-YELLOW is that 40 years ago, this was considered pornographic. Really, the sex and nudity scenes are few and far between, even then it\u0027s not shot like some cheaply made porno. While my countrymen mind find it shocking, in reality sex and nudity are a major staple in Swedish cinema. Even Ingmar Bergman, arguably their answer to good old boy John Ford, had sex scenes in his films.\u003cbr /\u003e\u003cbr /\u003eI do commend the filmmakers for the fact that any sex shown in the film is shown for artistic purposes rather than just to shock people and make money to be shown in pornographic theaters in America. I AM CURIOUS-YELLOW is a good film for anyone wanting to study the meat and potatoes (no pun intended) of Swedish cinema. But really, this film doesn\u0027t have much of a plot."
},
"id": "train_0",
"metadata": {},
"responses": {},
"status": "pending",
"suggestions": {},
"vectors": {}
}
```

While the same record in HuggingFace `datasets` looks as follows:

```json
{
"_server_id": "56fdf0c5-5720-408f-99f1-ae7b89d46242",
"id": "train_0",
"label_0.responses": null,
"label_0.responses.status": null,
"label_0.responses.users": null,
"status": "pending",
"text": "I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first it was seized by U.S. customs if it ever tried to enter this country, therefore being a fan of films considered \"controversial\" I really had to see this for myself.\u003cbr /\u003e\u003cbr /\u003eThe plot is centered around a young Swedish drama student named Lena who wants to learn everything she can about life. In particular she wants to focus her attentions to making some sort of documentary on what the average Swede thought about certain political issues such as the Vietnam War and race issues in the United States. In between asking politicians and ordinary denizens of Stockholm about their opinions on politics, she has sex with her drama teacher, classmates, and married men.\u003cbr /\u003e\u003cbr /\u003eWhat kills me about I AM CURIOUS-YELLOW is that 40 years ago, this was considered pornographic. Really, the sex and nudity scenes are few and far between, even then it\u0027s not shot like some cheaply made porno. While my countrymen mind find it shocking, in reality sex and nudity are a major staple in Swedish cinema. Even Ingmar Bergman, arguably their answer to good old boy John Ford, had sex scenes in his films.\u003cbr /\u003e\u003cbr /\u003eI do commend the filmmakers for the fact that any sex shown in the film is shown for artistic purposes rather than just to shock people and make money to be shown in pornographic theaters in America. I AM CURIOUS-YELLOW is a good film for anyone wanting to study the meat and potatoes (no pun intended) of Swedish cinema. But really, this film doesn\u0027t have much of a plot."
}
```

### Data Splits

The dataset contains a single split, which is `train`.

## 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 guidelines

[More Information Needed]

#### 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

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions

[More Information Needed]

搜集汇总

数据集介绍

构建方式

在情感分析领域，analisis-sentimientos-imdb数据集借助Argilla平台构建，该平台专为高效数据标注与人类反馈优化而设计。数据集以IMDb电影评论为文本基础，通过结构化字段定义与问题设置，系统化地收集了针对每条评论的情感极性标注。构建过程中，平台确保了数据记录的标准化存储，包括文本内容、元数据及标注响应，从而为情感分类任务提供了高质量的监督信号。

特点

该数据集的核心特征在于其专注于二分类情感分析，标注维度清晰限定于“积极”与“消极”两类情感极性。数据实例以原始英文评论呈现，保留了完整的文本语境与表达细节，便于模型学习自然语言中的情感线索。此外，数据集结构兼容HuggingFace生态，支持无缝加载至Argilla服务器进行可视化探索或直接通过datasets库访问，体现了灵活性与实用性并重的设计理念。

使用方法

使用该数据集时，研究人员可通过安装Argilla库并调用from_hub方法，将完整配置与记录推送至本地服务器，以便进行交互式标注与数据分析。若仅需访问数据实例，则可利用datasets库的load_dataset函数直接加载，快速获取文本与标注信息。这种双模式支持使得数据集既能服务于主动的标注工作流，也能适应于传统的机器学习管道，为情感分析模型的训练与评估提供便利。

背景与挑战

背景概述

情感分析作为自然语言处理领域的重要分支，旨在通过计算模型识别文本中蕴含的主观情感倾向。IMDb电影评论数据集自2000年代初由斯坦福大学研究人员构建以来，已成为情感分析任务的基础性资源，其规模与标注质量推动了机器学习模型在文本情感极性判别方面的突破性进展。该数据集通过收集互联网电影数据库的用户影评，并标注正面或负面情感标签，为监督学习算法提供了丰富的训练样本，深刻影响了情感分类、意见挖掘及推荐系统等相关研究方向的发展轨迹。

当前挑战

情感分析任务面临的核心挑战在于文本中情感表达的复杂性与语境依赖性，例如讽刺、双重否定及文化特定表述常导致模型误判。构建过程中，数据收集需处理噪声评论与垃圾信息，而人工标注则受限于标注者主观差异与情感边界模糊性，易引入标注不一致问题。此外，数据集的时效性与领域适应性亦构成持续挑战，早期标注可能无法涵盖新兴语言现象与跨文化情感表达变体。

常用场景

经典使用场景

在自然语言处理领域，情感分析作为一项基础任务，旨在从文本中自动识别情感倾向。analisis-sentimientos-imdb数据集聚焦于电影评论的情感分类，其经典使用场景在于为研究人员提供标注有正面或负面标签的英文影评文本，用于训练和评估情感分析模型。该数据集常被应用于监督学习框架下，通过构建分类器来区分评论的情感极性，从而推动情感分析技术的基准测试与性能优化。

实际应用

在实际应用层面，analisis-sentimientos-imdb数据集为电影产业的市场分析与用户反馈挖掘提供了有力工具。基于该数据集训练的模型能够自动化处理海量影评，识别观众情感趋势，辅助电影制作方优化内容策略。此外，在推荐系统与社交媒体监控中，此类情感分析技术也可用于提升用户体验，实现个性化服务，具有广泛的商业价值。

衍生相关工作

围绕该数据集，衍生了一系列经典研究工作。例如，基于IMDB影评的情感分析任务常被用作基准，推动了如BERT、RoBERTa等预训练语言模型在情感分类上的性能验证。同时，该数据集也激发了对抗性样本生成、领域自适应以及少样本学习等方法的探索，为自然语言处理领域的模型鲁棒性与泛化性研究提供了重要实验平台。