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--- |
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license: mit |
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task_categories: |
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- text-classification |
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language: |
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- en |
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tags: |
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- sentiment-analysis |
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- text-classification |
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- multiclass-classification |
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pretty_name: Sentiment Analysis Preprocessed Dataset including training and testing split |
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size_categories: |
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- 10K<n<100K |
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--- |
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**Brief idea about dataset**: |
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<br> |
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This dataset is designed for a Text Classification to be specific Multi Class Classification, inorder to train a model (Supervised Learning) for Sentiment Analysis. |
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<br> |
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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**. |
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**Main Features** |
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| text | labels | |
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|----------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| 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. | |
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**Other Features** |
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| preds | feedback | retrain_labels | retrained_preds | |
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|----------------------------------------------------------|--------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------| |
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| 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. | |
