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Mahsulot #:
91909029
Text Processing And Sentiment Analysis Using Machine Learning And Deep Learning With Python Gui
Mahsulot #:
91909029
UZS 619942
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Mahsulot tafsilotlari
- 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) |
MAHSULOT TAVSIFI
Text Processing And Sentiment Analysis Using Machine Learning And Deep Learning With Python Gui
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Savol: Ubuy'dan TEXT PROCESSING AND SENTIMENT ANALYSIS USING MACHINE onlayn xarid qilish qanday?
Javob: Ubuy'dan TEXT PROCESSING AND SENTIMENT ANALYSIS USING MACHINE onlayn xarid qilish oson.. Siz shunchaki mahsulotni qidirib topishingiz, tekshirish paytida jo'natish usulini tanlashingiz va uni manzilingizga yetkazib berishingiz kerak. -
Savol: TEXT PROCESSING AND SENTIMENT ANALYSIS USING MACHINE ichida onlayn xarid qilish mumkinmi?
Javob: Ha, Ubuy Uzbekistan’da ushbu mahsulot sizga mos narxda xarid qilishingiz mumkin.. TEXT PROCESSING AND SENTIMENT ANALYSIS USING MACHINE mahalliy hududda ishlamaydi, ammo tezkor yetkazib berish xizmatlarimizga ishonishingiz mumkin. -
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UZS 619942
Hoziroq buyurtma bering va oling Juma, Oktyabr 09
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