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Text Processing And Sentiment Analysis Using Machine Learning And Deep Learning With Python Gui

物品 #: 91909029

Text Processing And Sentiment Analysis Using Machine Learning And Deep Learning With Python Gui

物品 #: 91909029

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產品詳情

In this book, we explored a code implementation for sentiment analysis using machine learning models, including XGBoost, LightGBM, and LSTM. The code aimed to build, train, and evaluate these models on Twitter data to classify sentiments. Throughout the project, we gained insights into the key steps involved and observed the findings and functionalities of the code.Sentiment analysis is a vital task in natural language processing, and the code was to give a comprehensive approach to tackle it. The implementation began by checking if pre-trained models for XGBoost and LightGBM existed. If available, the models were loaded; otherwise, new models were built and trained. This approach allowed for reusability of trained models, saving time and effort in subsequent runs.Similarly, the code checked if preprocessed data for LSTM existed. If not, it performed tokenization and padding on the text data, splitting it into train, test, and validation sets. The preprocessed data was saved for future use.The code also provided a function to build and train the LSTM model. It defined the model architecture using the Keras Sequential API, incorporating layers like embedding, convolutional, max pooling, bidirectional LSTM, dropout, and dense output. The model was compiled with appropriate loss and optimization functions. Training was carried out, with early stopping implemented to prevent overfitting.After training, the model summary was printed, and both the model and training history were saved for future reference. The train_lstm function ensured that the LSTM model was ready for prediction by checking the existence of preprocessed data and trained models. If necessary, it performed the required preprocessing and model building steps.The pred_lstm() function was responsible for loading the LSTM model and generating predictions for the test data. The function returned the predicted sentiment labels, allowing for further analysis and evaluation.To facilitate user interaction, the code included a functionality to choose the LSTM model for prediction. The choose_prediction_lstm() function was triggered when the user selected the LSTM option from a dropdown menu. It called the pred_lstm() function, performed evaluation tasks, and visualized the results. Confusion matrices and true vs. predicted value plots were generated to assess the model's performance. Additionally, the loss and accuracy history from training were plotted, providing insights into the model's learning process.In conclusion, this project provided a comprehensive overview of sentiment analysis using machine learning models. The code implementation showcased the steps involved in building, training, and evaluating models like XGBoost, LightGBM, and LSTM. It emphasized the importance of data preprocessing, model building, and evaluation in sentiment analysis tasks. The code also demonstrated functionalities for reusing pre-trained models and saving preprocessed data, enhancing efficiency and ease of use. Through visualization techniques, such as confusion matrices and accuracy/loss curves, the code enabled a better understanding of the model's performance and learning dynamics. Overall, this project highlighted the practical aspects of sentiment analysis and illustrated how different machine learning models can be employed to tackle this task effectively.

Publisher	Independently published
Publication date	March 12, 2022
Language	English
Print length	333 pages
ISBN-13	979-8431335075
Item Weight	2.11 pounds (960 grams)
Dimensions	8.5 x 0.75 x 11 inches (21.6 x 1.9 x 27.9 cm)

產品敘述

Text Processing And Sentiment Analysis Using Machine Learning And Deep Learning With Python Gui

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題: 如何從 Ubuy 在線購物 Text Processing And Sentiment Analysis Using Machine？

回答: 從 Ubuy 在線購物 Text Processing And Sentiment Analysis Using Machine 非常簡單。. 您只需搜索產品，在結賬時選擇運輸方式，然後將其運送到您所在的位置。
題: Text Processing And Sentiment Analysis Using Machine 可以在 Hong Kong 在線購物嗎？

回答: 是的，您可以在 Ubuy Hong Kong 以合理的價格購買該產品。. Text Processing And Sentiment Analysis Using Machine 在本地不可用，但您可以信任我們的快遞服務。
題: 下訂單後需要多長時間才能收到產品？

回答: 您訂購的產品的交貨時間根據您訂購的商品和您選擇的運輸方式而有所不同。. 結賬時會提到預計送貨時間，所以購物時請放心。

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Text Processing And Sentiment Analysis Using Machine Learning And Deep Learning With Python Gui