Sentiment Analysis Using Deep Learning

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Sentiment Analysis Using Deep Learning

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Author : Miao Wei
language : en
Publisher:
Release Date : 2017

Sentiment Analysis Using Deep Learning written by Miao Wei and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Computer Science Theses categories.

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

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-06-26

Text Processing And Sentiment Analysis Using Machine Learning And Deep Learning With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-26 with Computers categories.

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.

Deep Learning For Social Media Data Analytics

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Author : Tzung-Pei Hong
language : en
Publisher: Springer Nature
Release Date : 2022-09-18

Deep Learning For Social Media Data Analytics written by Tzung-Pei Hong and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-18 with Computers categories.

This edited book covers ongoing research in both theory and practical applications of using deep learning for social media data. Social networking platforms are overwhelmed by different contents, and their huge amounts of data have enormous potential to influence business, politics, security, planning and other social aspects. Recently, deep learning techniques have had many successful applications in the AI field. The research presented in this book emerges from the conviction that there is still much progress to be made toward exploiting deep learning in the context of social media data analytics. It includes fifteen chapters, organized into four sections that report on original research in network structure analysis, social media text analysis, user behaviour analysis and social media security analysis. This work could serve as a good reference for researchers, as well as a compilation of innovative ideas and solutions for practitioners interested in applying deep learning techniques to social media data analytics.

Hotel Review Sentiment Analysis Using Machine Learning And Deep Learning With Python Gui

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-03-15

Hotel Review Sentiment Analysis Using Machine Learning And Deep Learning With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-15 with Computers categories.

The data used in this project is the data published by Anurag Sharma about hotel reviews that were given by costumers. The data is given in two files, a train and test. The train.csv is the training data, containing unique User_ID for each entry with the review entered by a costumer and the browser and device used. The target variable is Is_Response, a variable that states whether the costumers was happy or not happy while staying in the hotel. This type of variable makes the project to a classification problem. The test.csv is the testing data, contains similar headings as the train data, without the target variable. 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, and LSTM. Three vectorizers used in machine learning are Hashing Vectorizer, Count Vectorizer, and TFID Vectorizer. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.

Exploration Of Social Media For Sentiment Analysis Using Deep Learning

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Author : 陳良駒 (資訊管理)
language : en
Publisher:
Release Date : 2020

Exploration Of Social Media For Sentiment Analysis Using Deep Learning written by 陳良駒 (資訊管理) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.

Deep Learning And Its Applications

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Author : Dr. S. Manikandan
language : en
Publisher: Quing Publications
Release Date : 2022-12-30

Deep Learning And Its Applications written by Dr. S. Manikandan and has been published by Quing Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-30 with Computers categories.

Deep Learning and its Applications book chapter is intended to provide various deep insight about Deep learning in various applications. According to current Industry 4.0 standards, Deep learning on the emerging research area to give various services to IT and ITeS. In this book chapter various real time applications are taken for evaluating deep learning approach. Deep Learning is the subset of machine learning which has further learned results of artificial intelligent applications. Artificial Intelligent is the current scenario for making effective decisions. Here the applications are medical image processing, moving objects, image analysis, classification, clustering, prediction, and restoration used to identify various results. Based on each chapter different problems are taken for evaluation and apply different deep learning principles to find accuracy, precision, and score functions. Supervised and Unsupervised learning techniques, TensorFlow, Yolo classifier and Colabs are used to simulate the applications. In this book chapters are very useful for researchers, students, and faculty community to learn about Deep Learning in current trends.

Deep Learning Based Approaches For Sentiment Analysis

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Author : Basant Agarwal
language : en
Publisher: Springer Nature
Release Date : 2020-01-24

Deep Learning Based Approaches For Sentiment Analysis written by Basant Agarwal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-24 with Technology & Engineering categories.

This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.

Ai Based Data Analytics

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Author : Kiran Chaudhary
language : en
Publisher: CRC Press
Release Date : 2023-12-29

Ai Based Data Analytics written by Kiran Chaudhary and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-29 with Computers categories.

This book covers various topics related to marketing and business analytics. It explores how organizations can increase their profits by making better decisions in a timely manner through the use of data analytics. This book is meant for students, practitioners, industry professionals, researchers, and academics working in the field of commerce and marketing, big data analytics, and organizational decision-making. Highlights of the book include: The role of Explainable AI in improving customer experiences in e-commerce Sentiment analysis of social media Data analytics in business intelligence Federated learning for business intelligence AI-based planning of business management An AI-based business model innovation in new technologies An analysis of social media marketing and online impulse buying behaviour AI-Based Data Analytics: Applications for Business Management has two primary focuses. The first is on analytics for decision-making and covers big data analytics for market intelligence, data analytics and consumer behavior, and the role of big data analytics in organizational decision-making. The book’s second focus is on digital marketing and includes the prediction of marketing by consumer analytics, web analytics for digital marketing, smart retailing, and leveraging web analytics for optimizing digital marketing strategies.

Intelligent Strategies For Ict

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Author : M. Shamim Kaiser
language : en
Publisher: Springer Nature
Release Date : 2025-09-05

Intelligent Strategies For Ict written by M. Shamim Kaiser and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-05 with Technology & Engineering categories.

This book contains best selected research papers presented at ICTCS 2024: Ninth International Conference on Information and Communication Technology for Competitive Strategies. The conference will be held in Jaipur, India during 19 – 21 December 2024. The book covers state-of-the-art as well as emerging topics pertaining to ICT and effective strategies for its implementation for engineering and managerial applications. This book contains papers mainly focused on ICT for computation, algorithms and data analytics and IT security. The work is presented in ten volumes.

Distributed Computing And Intelligent Technology

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Author : Anisur Rahaman Molla
language : en
Publisher: Springer Nature
Release Date : 2023-01-08

Distributed Computing And Intelligent Technology written by Anisur Rahaman Molla and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-08 with Computers categories.

This book constitutes the proceedings of the 19th International Conference on Distributed Computing and Intelligent Technology, ICDCIT 2023, which was held in Bhubaneswar, India, in January 2023. The 20 full papers and 9 short papers presented in this volume were carefully reviewed and selected from 55 submissions. The papers are organized in the following topical sections: Invited Talks; Distributed Computing; Intelligent Technology.