Datasets:
| 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**: | |
| <br> | |
| This dataset is designed for a Text Classification to be specific Multi Class Classification, inorder to train a model (Supervised Learning) for Sentiment Analysis. | |
| <br> | |
| 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. | |