Machine Learning For Emotion Analysis In Python
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Machine Learning For Emotion Analysis In Python
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Author : Allan Ramsay
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-09-28
Machine Learning For Emotion Analysis In Python written by Allan Ramsay and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-28 with Computers categories.
Kickstart your emotion analysis journey with this step-by-step guide to data science success Key Features Discover the inner workings of the end-to-end emotional analysis workflow Explore the use of various ML models to derive meaningful insights from data Hone your craft by building and tweaking complex emotion analysis models with practical projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionArtificial intelligence and machine learning are the technologies of the future, and this is the perfect time to tap into their potential and add value to your business. Machine Learning for Emotion Analysis in Python helps you employ these cutting-edge technologies in your customer feedback system and in turn grow your business exponentially. With this book, you’ll take your foundational data science skills and grow them in the exciting realm of emotion analysis. By following a practical approach, you’ll turn customer feedback into meaningful insights assisting you in making smart and data-driven business decisions. The book will help you understand how to preprocess data, build a serviceable dataset, and ensure top-notch data quality. Once you’re set up for success, you’ll explore complex ML techniques, uncovering the concepts of deep neural networks, support vector machines, conditional probabilities, and more. Finally, you’ll acquire practical knowledge using in-depth use cases showing how the experimental results can be transformed into real-life examples and how emotion mining can help track short- and long-term changes in public opinion. By the end of this book, you’ll be well-equipped to use emotion mining and analysis to drive business decisions.What you will learn Distinguish between sentiment analysis and emotion analysis Master data preprocessing and ensure high-quality input Expand the use of data sources through data transformation Design models that employ cutting-edge deep learning techniques Discover how to tune your models’ hyperparameters Explore the use of naive Bayes, SVMs, DNNs, and transformers for advanced use cases Practice your newly acquired skills by working on real-world scenarios Who this book is forThis book is for data scientists and Python developers looking to gain insights into the customer feedback for their product, company, brand, governorship, and more. Basic knowledge of machine learning and Python programming is a must.
Machine And Deep Learning Techniques For Emotion Detection
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Author : Rai, Mritunjay
language : en
Publisher: IGI Global
Release Date : 2024-05-14
Machine And Deep Learning Techniques For Emotion Detection written by Rai, Mritunjay and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-14 with Psychology categories.
Computer understanding of human emotions has become crucial and complex within the era of digital interaction and artificial intelligence. Emotion detection, a field within AI, holds promise for enhancing user experiences, personalizing services, and revolutionizing industries. However, navigating this landscape requires a deep understanding of machine and deep learning techniques and the interdisciplinary challenges accompanying them. Machine and Deep Learning Techniques for Emotion Detection offer a comprehensive solution to this pressing problem. Designed for academic scholars, practitioners, and students, it is a guiding light through the intricate terrain of emotion detection. By blending theoretical insights with practical implementations and real-world case studies, our book equips readers with the knowledge and tools needed to advance the frontier of emotion analysis using machine and deep learning methodologies.
Emotion Prediction From Text Using Machine Learning And Deep Learning With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-06-28
Emotion Prediction From Text 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 2023-06-28 with Computers categories.
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 model's 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 model's 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.
Iot Cloud And Data Science
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Author : S. Prasanna Devi
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2023-02-27
Iot Cloud And Data Science written by S. Prasanna Devi and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-27 with Computers categories.
Selected peer-reviewed full text papers from the International Research Conference on IoT, Cloud and Data Science (IRCICD'22) Selected peer-reviewed full text papers from the International Research Conference on IoT, Cloud and Data Science (IRCICD'22), May 06-07, 2022, Chennai, India
Multi Label Emotion Classification Using Machine Learning And Deep Learning Methods
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Author : Drashtikumari Kher
language : en
Publisher:
Release Date : 2021
Multi Label Emotion Classification Using Machine Learning And Deep Learning Methods written by Drashtikumari Kher and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Emotion detection in online social networks benefits many applications like personalized advertisement services, suggestion systems, etc. Emotion can be identified from various sources like text, facial expressions, images, speeches, paintings, songs, etc. Emotion detection can be done by various techniques in machine learning. Traditional emotion detection techniques mainly focus on multi-class classification while ignoring the co-existence of multiple emotion labels in one instance. This research work is focussed on classifying multiple emotions from data to handle complex data with the help of different machine learning and deep learning methods. Before modeling, first data analysis is done and then the data is cleaned. Data pre-processing is performed in steps such as stop-words removal, tokenization, stemming and lemmatization, etc., which are performed using a Natural Language Processing toolkit (NLTK). All the input variables are converted into vectors by naive text encoding techniques like word2vec, Bag-of-words, and term frequency-inverse document frequency (TF-IDF). This research is implemented using python programming language. To solve multi-label emotion classification problem, machine learning and deep learning methods were used. The evaluation parameters such as accuracy, precision, recall, and F1-score were used to evaluate the performance of the classifiers Naïve Bayes, support vector machine (SVM), Random Forest, K-nearest neighbour (KNN), GRU (Gated Recurrent Unit) based RNN (Recurrent Neural Network) with Adam optimizer and Rmsprop optimizer. GRU based RNN with Rmsprop optimizer achieves an accuracy of 82.3%, Naïve Bayes achieves highest precision of 0.80, Random Forest achieves highest recall score of 0.823, SVM achieves highest F1 score of 0.798 on the challenging SemEval2018 Task 1: E-c multi-label emotion classification dataset. Also, One-way Analysis of Variance (ANOVA) test was performed on the mean values of performance metrics (accuracy, precision, recall, and F1-score) on all the methods.
Sentiment Analysis With Python A Hands On Approach
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Author : Dr. G. UMA DEVI
language : en
Publisher: Magestic Technology Solutions (P) Ltd
Release Date : 2023-02-21
Sentiment Analysis With Python A Hands On Approach written by Dr. G. UMA DEVI and has been published by Magestic Technology Solutions (P) Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-21 with Computers categories.
Sentiment Analysis with Python: A Hands-on Approach is a practical guide to building sentiment analysis solutions using Natural Language Processing (NLP) techniques and Python. It introduces the fundamentals of sentiment analysis and then takes the reader through the complete workflow required to convert raw text into reliable sentiment insights. The book explains essential text preparation methods such as tokenization, normalization, stop-word removal, stemming/lemmatization, and handling emojis, slangs, abbreviations, and negations. It then covers feature engineering techniques including Bag of Words, TF–IDF, and word/sentence embeddings. Multiple sentiment analysis strategies are presented—lexicon-based and rule-based methods, machine learning approaches, and deep learning models—followed by clear guidance on evaluating models using metrics such as accuracy, precision/recall, F1 score, confusion matrix, ROC/AUC, and Cohen’s kappa. To connect theory with practice, the text includes hands-on Python examples, domain-focused applications (social media, news, customer reviews, and finance), and advanced topics such as transfer learning, multilingual sentiment analysis, multimodal sentiment analysis, and handling inconsistent data, concluding with future directions and recommendations. Book details: Title: Sentiment Analysis with Python: A Hands-on Approach Author: Dr. G. Uma Devi First published: February 2023 Edition: First Edition Publisher: Magestic Technology Solutions (P) Ltd., Chennai, Tamil Nadu, India ISBN (Paperback): 978-93-92090-11-0 DOI: https://www.doi.org/10.47716/MTS.B.978-93-92090-11-0 Pages: 178 (Front pages 12; Inner pages 166)
Machine Learning And Python For Human Behavior Emotion And Health Status Analysis
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Author : Md Zia Uddin
language : en
Publisher: CRC Press
Release Date : 2024-08-30
Machine Learning And Python For Human Behavior Emotion And Health Status Analysis written by Md Zia Uddin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-30 with Computers categories.
This book is a practical guide for individuals interested in exploring and implementing smart home applications using Python. Comprising six chapters enriched with hands-on codes, it seamlessly navigates from foundational concepts to cutting-edge technologies, balancing theoretical insights and practical coding experiences. In short, it is a gateway to the dynamic intersection of Python programming, smart home technology, and advanced machine learning applications, making it an invaluable resource for those eager to explore this rapidly growing field. Key Features: Throughout the book, practicality takes precedence, with hands-on coding examples accompanying each concept to facilitate an interactive learning journey Striking a harmonious balance between theoretical foundations and practical coding, the book caters to a diverse audience, including smart home enthusiasts and researchers The content prioritizes real-world applications, ensuring readers can immediately apply the knowledge gained to enhance smart home functionalities Covering Python basics, feature extraction, deep learning, and XAI, the book provides a comprehensive guide, offering an overall understanding of smart home applications
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.
Machine Learning
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Author :
language : en
Publisher:
Release Date : 2017
Machine Learning written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
"Use Python and the Twitter API to build your own sentiment analyzer. Sentiment analysis, or opinion mining, is a field of neuro-linguistic programming that deals with extracting subjective information, like positive/negative, like/dislike, and emotional reactions. In this Twitter sentiment analysis in Python online course, you'll learn real examples of why sentiment analysis is important and how to approach specific problems using sentiment analysis. Learn why sentiment analysis is useful and how to approach the problem using both rule-based and machine learning-based approaches. The details are really important - training data and feature extraction are critical. Sentiment Lexicons provide us with lists of words in different sentiment categories that we can use for building our feature set. All this is in the run up to a serious project to perform Twitter sentiment analysis. We'll spend some time on regular expressions which are pretty handy to know as we'll see in our code-along."--Resource description page.
Machine Learning Approaches To Emotion Recognition Searching For A Hierarchy In Emotions
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Author : Barbara Verstraeten
language : en
Publisher:
Release Date : 2013
Machine Learning Approaches To Emotion Recognition Searching For A Hierarchy In Emotions written by Barbara Verstraeten and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Automatic emotion recognition is part of the wider framework of sentiment analysis and deals with the computational treatment of emotions in text. Some studies have brought the idea forward of a hierarchy in human emotion detection. We tried to see if a machine learning approach could provide insights on the difference and similarities of emotions. We approached the problem as a text classification task and employed simple feature vectors constructed with the Natural Language ToolKit. We used two supervised classifiers: Naïve Bayes and Support Vector Machines. Data was made available by Aman & Szpakowicz (2007). The data was annotated for seven emotion categories: happiness, sadness, anger, fear, disgust, surprise and no emotion. The highest obtained accuracy for the entire data was 94,23% using a SVM on balanced data, the lowest accuracy was 46,11% for Naïve Bayes on unbalanced data. Performance differed greatly between classifiers and if data was resampled or not.