license:mit size_categories:n<1K format:csv modality:text library:datasets library:pandas library:mlcroissant library:polars region:us infinite-dataset-hub synthetic
---
license: mit
tags:
- infinite-dataset-hub
- synthetic
---

# SportsFanEngagement

tags: sentiment analysis, fan interaction, predictive modeling

_Note: This is an AI-generated dataset so its content may be inaccurate or false_

**Dataset Description:**

The 'SportsFanEngagement' dataset comprises of anonymized user-generated content from various social media platforms. The dataset is aimed at understanding fan interaction and sentiment towards different sports teams and events. It is structured to facilitate analysis for sentiment analysis and predictive modeling purposes, aiming to gauge fan engagement and predict future trends in fan behavior.

Each row represents a unique piece of user content (such as a tweet, Facebook post, or Reddit comment), along with metadata and a label indicating the sentiment and type of engagement (e.g., positive support, neutral observation, negative critique). The label 'positive' indicates a supportive or enthusiastic tone towards the sports entity, 'neutral' indicates a non-partisan or informational tone, and 'negative' signifies critical or disdainful content.

**CSV Content Preview:**

```
Timestamp,Content,Label
2023-04-01T12:34:56Z,"Can't believe how well the team played last night! Totally nail-biting finish! #SoccerFever",positive
2023-04-01T13:21:37Z,"The new jerseys are not really my style, I prefer the old ones.",neutral
2023-04-01T13:45:22Z,"The referee's call was so questionable, it ruined the game!",negative
2023-04-01T14:05:15Z,"Just finished my first marathon, feel great! The sports team hosted the event, awesome atmosphere!",positive
2023-04-01T14:22:00Z,"Honestly, I've never understood the appeal of this sport. #BoringGame",neutral
```

This dataset provides a variety of expressions related to sports fandom that can be used to train machine learning models for sentiment analysis and to understand fan engagement patterns. It is essential for ML practitioners to curate the dataset with a balanced distribution of sentiment labels to avoid biases in predictive modeling.

**Source of the data:**

The dataset was generated using the [Infinite Dataset Hub](https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub) and microsoft/Phi-3-mini-4k-instruct using the query '':

- **Dataset Generation Page**: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub?q=&dataset=SportsFanEngagement&tags=sentiment+analysis,+fan+interaction,+predictive+modeling
- **Model**: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
- **More Datasets**: https://huggingface.co/datasets?other=infinite-dataset-hub
