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Datasets:
prasadsawant7
/
sentiment_analysis_preprocessed_dataset

Tasks:
Text Classification
Modalities:
Tabular
Text
Formats:
csv
Languages:
English
Size:
100K - 1M
Tags:
sentiment-analysis
text-classification
multiclass-classification
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files Community
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@@ -20,17 +20,15 @@ This dataset is designed for a Text Classification to be specific Multi Class Cl
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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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  <br>
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- <br>
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  **Main Features**
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- <br>
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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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  |----------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|
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  <br>
 
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  **Other Features**
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- <br>
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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. |
 
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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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  <br>
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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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  |----------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|
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  <br>
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+
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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. |