Dark Mode
Synthetic Tweet Sentiment Analysis Collection Dataset
Fraud Detection & Risk Management
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset is a collection of synthetic, yet realistic, tweet data designed to address the challenges of accessing actual tweet datasets. Each entry simulates an authentic tweet tone and is categorised with one of three sentiments: positive, neutral, or negative. Its primary purpose is to facilitate the development and comparison of Natural Language Processing (NLP) models for sentiment analysis and text classification.
Columns
- tweet: This column contains the full text content of the simulated tweet.
- sentiment: This column provides the labelled sentiment for each tweet, indicating whether it is positive, neutral, or negative.
Distribution
The data files are typically provided in CSV format. Specific figures for the total number of rows or records are not detailed in the available information.
Usage
This dataset is ideal for a variety of applications, including:
- Building and training NLP classification models.
- Practising text preprocessing techniques and conducting sentiment analysis.
- Comparing the performance of different machine learning algorithms on text-based data.
Coverage
The dataset's coverage is global, simulating general tweet content without specific geographic or demographic limitations. As a synthetic dataset, it focuses on realistic linguistic representation rather than specific real-world event timelines.
License
CC-BY-SA
Who Can Use It
This dataset is suitable for:
- Data scientists and machine learning engineers working on text analytics.
- NLP researchers and students learning about sentiment analysis and text classification.
- Developers creating applications that require sentiment detection capabilities.
Dataset Name Suggestions
- Synthetic Tweet Sentiment Data
- Realistic Tweet Sentiment Analysis Collection
- Social Media Sentiment Classifier Dataset
- Tweet Emotion Classification Set
Attributes
Original Data Source: Tweet Sentiment Classification Dataset
QUALITY