عرض الأصل

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

This project provided a comprehensive overview of sentiment analysis using state-of-the-art machine learning models.

منتج #: 91909029

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

منتج #: 91909029

LYD 310

تفاصيل السعر

باستثناء رسوم الشحن والجمارك ( سيتم احتساب رسوم الشحن والجمارك عند إتمام الشراء )

^{*سيتم استيراد جميع العناصر من أمريكا}

التوافر : متوفر فى المخزون

مستورد من متجر USA

كمية:

اطلب الآن واحصل عليه حول الجمعة, أكتوبر 09

معاملة آمنة

أفضل شركائنا اللوجستيين

This project provided a comprehensive overview of sentiment analysis using state-of-the-art machine learning models.

Paperback

كفالة يو كير:

لا شيء

اختر الباقة

شحن
سريع

استرجاع
مجاني^*

تغليف آمن

منتجات أصلية %100

الامتثال لمعيار PCI DSS

حاصل على شهادة ISO 27001

أبرز ما يلفت الانتباه

User-Friendly Interface

The Python GUI ensures an intuitive user experience, making complex text processing and sentiment analysis accessible for users with varying levels of expertise, from beginners to advanced practitioners.

Advanced Algorithms

Utilizes cutting-edge machine learning and deep learning techniques, providing robust and accurate sentiment analysis that outperforms traditional methods, effectively addressing complex text data challenges.

Comprehensive Tutorials

Includes detailed, step-by-step tutorials that guide users through the implementation of text processing techniques, enhancing learning and practical application, making it valuable for educators and learners alike.

تفاصيل المنتج

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)

من يجب أن يشتري؟

Data Scientists
Ideal for data scientists who need to analyze text data and derive insights using advanced machine learning techniques.
Business Analysts
Useful for business analysts looking to assess customer sentiments to inform marketing strategies and improve customer satisfaction.
Developers Learning ML
Great for developers who want to learn about machine learning and deep learning frameworks in a practical, hands-on environment.

Absolute Beginners
Not suitable for absolute beginners with no background in programming or machine learning concepts, as it may overwhelm them.

وصف المنتج

هل لديك أي استفسار؟ تحدث معنا

أسئلة العملاء & الإجابات

سؤال: What machine learning models are covered in this book?

إجابه: The book covers XGBoost, LightGBM, and LSTM for sentiment analysis.
سؤال: Is prior knowledge of Python required?

إجابه: Basic Python knowledge is recommended, but the book provides detailed code explanations.
سؤال: Can the models be reused for future analyses?

إجابه: Yes, the implementation allows for the loading of pre-trained models and preprocessed data.

Intelligence & Semantics Editorial Review

لم يتم العثور على مراجعات تحريرية

مراجعات العملاء وتقييماتهم

5.0

1 تقييمات العملاء

5 نجمة

100%
4 نجمة

0%
3 نجمة

0%
2 نجمة

0%
1 نجمة

0%

أضف تقييم لهذا المنتج

شارك أفكارك مع عملاء آخرين

منصة موثوقة وثقة كاملة للمشتري

4.2/5 9477 التقييمات

اطّلع على التقييمات

Jasmin

مشتري موثّق

“Great products and very good service: very easy and very fast international delivery.”

9 September 2026 · عبر Trustpilot

Anke

مشتري موثّق

“Wonderful online shopping experience, smooth transaction from the start. Payment method works conveniently and delivery is unexpectedly fast and reliable. You go the extra mile for service. What makes this even more amazing, you deliver to Namibia. I will remain a happy Ubuy customer and will increase my purchases for sure! Thank you!”

10 September 2026 · عبر Trustpilot

Hazel

مشتري موثّق

“Very easy to find the products what you need, and so fast delivery, that’s why I highly recommended to others costumers to used ubuy.”

10 September 2026 · عبر Trustpilot

Opaleye

مشتري موثّق

“I received exactly what I ordered I was skeptical about your site because that was my first time to order. But the order came timely and neatly packaged. I was not disappointed. Thank you.”

11 September 2026 · عبر Trustpilot

Adele

مشتري موثّق

“Easy to find and order what you want on the website. Delivery is quick to the UK”

8 September 2026 · عبر Trustpilot

الخروج الآمن شحن عالمي إسترجاع سهل منتجات أصلية

تاريخ سعر المنتج

معلومات مهمة

القيود: بالنسبة للمنتجات التي يتم شحنها دولياً، يُرجى ملاحظة أن أي ضمان من الشركة المصنعة قد لا يكون صالحاً؛ قد لا تتوفر خيارات خدمة الشركة المصنعة؛ قد لا تكون أدلة المنتج والتعليمات وتحذيرات السلامة مكتوبة بلغة بلد المقصد؛ قد لا يتم تصميم المنتجات (والمواد المصاحبة لها) وفقاً لمعايير بلد الوجهة والمواصفات ومتطلبات الملصقات؛ وقد لا تتوافق المنتجات مع الجهد الكهربي المستخدم في بلد الوجهة والمعايير الكهربائية الأخرى (تتطلب استخدام محوّل كهربي أو جهاز تحويل إذا كان ذلك مناسباً). المستلم مسؤول عن ضمان إمكانية استيراد المنتج بشكل قانوني إلى بلد الوجهة. عند الطلب من يوباي أو الشركات التابعة لها، يكون المستلم هو المستورد المسجل ويجب أن يلتزم بجميع القوانين واللوائح الخاصة ببلد الوجهة.
ليست كل المنتجات المدرجة على يوباي معروضة للبيع، لأن يوباي هو محرك بحث عالمي. المنتجات تخضع للوائح التصدير / التجارة.

LYD 310

اطلب الآن واحصل عليه حول الجمعة, أكتوبر 09

كمية:

معاملة آمنة

نوفر لك مدفوعات مشفّرة، وحماية متكاملة للمشتري، مع الالتزام بمعايير PCI DSS وشهادة ISO 27001:2022 لضمان أعلى مستويات الأمان في كل عملية شراء.

أفضل شركائنا اللوجستيين

أسرع توصيل عبر الحدود

المميزات والفوائد

Explore sentiment analysis with machine learning and deep learning models.
Includes implementations for XGBoost, LightGBM, and LSTM using Python GUI.
Learn to preprocess, build, train, and evaluate models on Twitter data.
User-friendly features include model selection and visualization of results.
Save time and effort by using pre-trained models and preprocessing data.
Gain insights into key processes in natural language processing.

ضمان Ubuy

تسوّق بثقة مع منتجات أصلية %100، ومدفوعات آمنة متوافقة مع معيار PCI DSS، وحماية بيانات معتمدة وفق ISO 27001، وشحن دولي سريع، وإرجاع مجاني^*، وتغليف آمن لكل طلب.

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