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prasadsawant7/sentiment_analysis_preprocessed_dataset

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… See the full description on the dataset page: https://huggingface.co/datasets/prasadsawant7/sentiment_analysis_preprocessed_dataset.

sourceHugging Facemitupdated 3y agoView on Hugging Face
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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 | retrainlabels | retrainedpreds | |----------------------------------------------------------|--------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------| | 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. |