README.md

metadata

license: mit
task_categories:
  - text-classification
language:
  - en
tags:
  - sentiment-analysis
  - text-classification
  - multiclass-classification
pretty_name: Sentiment Analysis Preprocessed Dataset including training and testing split
size_categories:
  - 10K<n<100K

Brief idea about dataset:
This dataset is designed for a Text Classification to be specific Multi Class Classification, inorder to train a model (Supervised Learning) for Sentiment Analysis.
Also to be able retrain the model on the given feedback over a wrong predicted sentiment this dataset will help to manage those things using Other Features.

**Main Features**
| text | labels | |----------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------| | This feature variable has all sort of texts, sentences, tweets, etc. | This target variable contains 3 types of numeric values as sentiments such as 0, 1 and 2. Where 0 means Negative, 1 means Neutral and 2 means Positive. |
**Other Features**
| preds | feedback | retrain_labels | retrained_preds | |----------------------------------------------------------|--------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------| | In this variable all predictions are going to be stored. | In this variable user can enter either yes or no to indicate whether the prediction is right or wrong. | In this variable user will enter the correct label as a feedback inorder to retrain the model. | In this variable all predictions after feedback loop are going to be stored. |