EMOTION PREDICTION FROM TEXT USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI

Name: EMOTION PREDICTION FROM TEXT USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI
Brand: BALIGE PUBLISHING
SKU: 01009793

Cargando comentarios…

$205

Envío gratis siempre

Comentarios

Cargando el resumen…

Por favor, inicia sesión para escribir un comentario.

Cargando comentarios…

1014090EMOTION PREDICTION FROM TEXT USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUIhttps://www.gandhi.com.mx/emotion-prediction-from-text-using-machine-learning-and-deep-learning-with-python-gui/phttps://gandhi.vtexassets.com/arquivos/ids/686626/6d92ae1e-ccbd-4c5b-b667-21133338e328.jpg?v=638335797462630000205205MXNBALIGE PUBLISHINGInStock/Ebooks/1009793EMOTION PREDICTION FROM TEXT USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI205205https://www.gandhi.com.mx/emotion-prediction-from-text-using-machine-learning-and-deep-learning-with-python-gui/phttps://gandhi.vtexassets.com/arquivos/ids/686626/6d92ae1e-ccbd-4c5b-b667-21133338e328.jpg?v=638335797462630000InStockMXN99999DIEbook20221230005536146_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1230005536146_In the dataset used in this project, there are two columns, Text and Emotion. Quite self-explanatory. The Emotion column has various categories ranging from happiness to sadness to love and fear. You will build and implement machine learning and deep learning models which can identify what words denote what emotion.The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy.(*_*)1230005536146_This is a captivating book that delves into the intricacies of building a robust system for emotion detection in textual data. Throughout this immersive exploration, readers are introduced to the methodologies, challenges, and breakthroughs in accurately discerning the emotional context of text.The book begins by highlighting the importance of emotion detection in various domains such as social media analysis, customer sentiment evaluation, and psychological research. Understanding human emotions in text is shown to have a profound impact on decision-making processes and enhancing user experiences.Readers are then guided through the crucial stages of data preprocessing, where text is carefully cleaned, tokenized, and transformed into meaningful numerical representations using techniques like Count Vectorization, TF-IDF Vectorization, and Hashing Vectorization.Traditional machine learning models, including Logistic Regression, Random Forest, XGBoost, LightGBM, and Convolutional Neural Network (CNN), are explored to provide a foundation for understanding the strengths and limitations of conventional approaches.However, the focus of the book shifts towards the Long Short-Term Memory (LSTM) model, a powerful variant of recurrent neural networks. Leveraging word embeddings, the LSTM model adeptly captures semantic relationships and long-term dependencies present in text, showcasing its potential in emotion detection.The LSTM models exceptional performance is revealed, achieving an astounding accuracy of 86 on the test dataset. Its ability to grasp intricate emotional nuances ingrained in textual data is demonstrated, highlighting its effectiveness in capturing the rich tapestry of human emotions.In addition to the LSTM model, the book also explores the Convolutional Neural Network (CNN) model, which exhibits promising results with an accuracy of 85 on the test dataset. The CNN model excels in capturing local patterns and relationships within the text, providing valuable insights into emotion detection.To enhance usability, an intuitive training and predictive interface is developed, enabling users to train their own models on custom datasets and obtain real-time predictions for emotion detection. This interactive interface empowers users with flexibility and accessibility in utilizing the trained models.The book further delves into the performance comparison between the LSTM model and traditional machine learning models, consistently showcasing the LSTM models superiority in capturing complex emotional patterns and contextual cues within text data.Future research directions are explored, including the integration of pre-trained language models such as BERT and GPT, ensemble techniques for further improvements, and the impact of different word embeddings on emotion detection. Practical applications of the developed system and models are discussed, ranging from sentiment analysis and social media monitoring to customer feedback analysis and psychological research. Accurate emotion detection unlocks valuable insights, empowering decision-making processes and fostering meaningful connections.In conclusion, this project encapsulates a transformative expedition into understanding human emotions in text. By harnessing the power of machine learning techniques, the book unlocks the potential for accurate emotion detection, empowering industries to make data-driven decisions, foster connections, and enhance user experiences. This book serves as a beacon for researchers, practitioners, and enthusiasts venturing into the captivating world of emotion detection in text....1230005536146_BALIGE PUBLISHINGlibro_electonico_544ab9fe-7cf9-3053-aafa-e1f4f3a0d5e9_1230005536146;1230005536146_1230005536146Rismon HasiholanInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/f2ca928b-958d-498f-bb70-f76c6615d8f6-epub-fdb02a23-911b-49d5-b167-b8b739f390bd.epub2022-04-20T00:00:00+00:00BALIGE PUBLISHING