Isbn: 9786209879531 - contextual emotion classification in text using hybrid word2vec-bilstm: deep learning approach (9 risultati)

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condizione: Neu. Contextual Emotion Classification in Text Using Hybrid Word2Vec-BiLSTM | Deep Learning Approach | Nilla Sivasrinu (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209879531 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.…

Paperback. Condizione: new. Paperback. In the modern digital era, human communication has shifted significantly toward unstructured text found on social media, micro-blogs, and instant messaging platforms. While traditional face-to-face interactions rely on non-verbal cues like facial expressions and vocal tone, digital text lacks these signals, making the detection of emotional intent a complex challenge for Natural Language Processing (NLP). This study addresses the limitations of traditional machine learning methods, such as Naive Bayes and Logistic Regression, which often fail to capture contextual meaning and long-range sequential dependencies. The proposed research introduces a hybrid deep learning framework that integrates Word2Vec(Continuous Bag-of-Words) embeddings with a Bidirectional Long Short-Term Memory (BiLSTM) network. Word2Vec is utilized to extract dense semantic features, enabling the model to understand mathematical relationships between words. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 116 pp. Englisch.

Paperback. Condizione: new. Paperback. In the modern digital era, human communication has shifted significantly toward unstructured text found on social media, micro-blogs, and instant messaging platforms. While traditional face-to-face interactions rely on non-verbal cues like facial expressions and vocal tone, digital text lacks these signals, making the detection of emotional intent a complex challenge for Natural Language Processing (NLP). This study addresses the limitations of traditional machine learning methods, such as Naive Bayes and Logistic Regression, which often fail to capture contextual meaning and long-range sequential dependencies. The proposed research introduces a hybrid deep learning framework that integrates Word2Vec(Continuous Bag-of-Words) embeddings with a Bidirectional Long Short-Term Memory (BiLSTM) network. Word2Vec is utilized to extract dense semantic features, enabling the model to understand mathematical relationships between words. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 116 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In the modern digital era, human communication has shifted significantly toward unstructured text found on social media, micro-blogs, and instant messaging platforms. While traditional face-to-face interactions rely on non-verbal cues like facial expressions and vocal tone, digital text lacks these signals, making the detection of emotional intent a complex challenge for Natural Language Processing (NLP). This study addresses the limitations of traditional machine learning methods, such as Naïve Bayes and Logistic Regression, which often fail to capture contextual meaning and long-range sequential dependencies. The proposed research introduces a hybrid deep learning framework that integrates Word2Vec(Continuous Bag-of-Words) embeddings with a Bidirectional Long Short-Term Memory (BiLSTM) network. Word2Vec is utilized to extract dense semantic features, enabling the model to understand mathematical relationships between words.…