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Textual Emotion Classifier Data
Data Science and Analytics
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About
Exploring emotions through text is a key area in human communication. This data offers a foundation for automatic emotion recognition from written content, which holds considerable practical value across various fields. It provides a valuable resource for developing and evaluating models capable of identifying emotional states expressed in text, thereby supporting advanced analytical applications.
Columns
- text: This column contains the raw text strings from which emotions are to be recognised. It comprises approximately 283,000 unique text entries.
- emotion: This column categorises the emotion expressed in the corresponding 'text' entry. It uses numerical labels where '0' represents 'sad' and '1' represents 'happy'. This column has two unique values, equally representing approximately 283,000 records.
Distribution
The data is presented in a CSV file format, named
Text_Emotion.csv, with a file size of 19.62 MB. It is structured into two columns and contains approximately 283,000 records or rows, with all entries being valid and without missing values.Usage
This data is ideally suited for training and evaluating machine learning models focused on text emotion recognition. It can be applied in areas such as sentiment analysis to gauge public opinion, social media monitoring to understand user sentiments, and customer feedback analysis to assess satisfaction and identify areas for improvement. Its structure also makes it suitable for binary classification tasks.
Coverage
The data primarily consists of English language text. Specific geographic, time range, or demographic scopes are not detailed in the provided information. However, its broad applicability to English text suggests global relevance wherever English communication occurs.
License
CC0: Public Domain
Who Can Use It
This data is highly beneficial for data scientists, machine learning engineers, and researchers interested in natural language processing and sentiment analysis. It is also suitable for students and beginners looking to gain practical experience in deep learning, text classification, and building emotion recognition models. Developers working on applications for social media monitoring or customer service analytics will find this data useful for model development.
Dataset Name Suggestions
- Textual Emotion Classifier Data
- Emotion in Text Dataset
- Happy Sad Text Corpus
- Emotion Recognition Text Samples
- Binary Text Emotion Data
Attributes
Original Data Source: Textual Emotion Classifier Data
QUALITY