Kadupu durga prasad (42 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.

Condizione: New.

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

Taschenbuch. Condizione: Neu. Deep Learning Sentiment Analysis of Hotel Reviews with BiLSTM | Deep Learning-Based Sentiment Analysis of Hotel Reviews Using LSTM and Bidirectional LSTM Models | Kadupu Durga Prasad (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209341229 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.…

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: New.

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

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

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

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

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

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

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

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

Paperback. Condizione: new. Paperback. In the digital era, the rapid growth of online platforms has significantly transformed the hospitality industry, where customer decisions are increasingly influenced by user-generated reviews. These reviews provide valuable insights into customer experiences; however, the vast volume of unstructured textual data makes manual analysis inefficient and impractical. To address this challenge, this study proposes an automated sentiment analysis system using deep learning techniques to classify hotel reviews into positive and negative sentiments.The research utilizes a large-scale dataset comprising over 500,000 hotel reviews, which undergoes extensive preprocessing, including text cleaning, tokenization, stopword removal, and data balancing to ensure model reliability. Exploratory Data Analysis (EDA) is conducted to understand data distribution and extract meaningful patterns. The processed textual data is then transformed into numerical representations using tokenization and sequence padding techniques.Two deep learning models, Long Short-Term Memory (LSTM) and Bidirectional Long ShortTerm Memory (BiLSTM), are implemented to capture sequential dependencies and contextual relationships. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Paperback. Condizione: new. Paperback. A l'ere du numerique, la croissance rapide des plateformes en ligne a profondement transforme le secteur de l'hotellerie, ou les decisions des clients sont de plus en plus influencees par les avis generes par les utilisateurs. Ces avis fournissent des informations precieuses sur les experiences des clients; cependant, le volume considerable de donnees textuelles non structurees rend l'analyse manuelle inefficace et peu pratique. Pour relever ce defi, cette etude propose un systeme automatise d'analyse des sentiments utilisant des techniques d'apprentissage profond afin de classer les avis sur les hotels en sentiments positifs et negatifs. La recherche s'appuie sur un ensemble de donnees a grande echelle comprenant plus de 500 000 avis sur des hotels, qui font l'objet d'un pretraitement approfondi, incluant le nettoyage du texte, la tokenisation, la suppression des mots vides et l'equilibrage des donnees afin de garantir la fiabilite du modele. Une analyse exploratoire des donnees (EDA) est realisee afin de comprendre la distribution des donnees et d'en extraire des tendances significatives. Les donnees textuelles traitees sont ensuite transformees en representations numeriques a l'aide de techniques de tokenisation et de remplissage de sequences. 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 112 pp. Englisch.

Paperback. Condizione: new. Paperback. In the digital era, the rapid growth of online platforms has significantly transformed the hospitality industry, where customer decisions are increasingly influenced by user-generated reviews. These reviews provide valuable insights into customer experiences; however, the vast volume of unstructured textual data makes manual analysis inefficient and impractical. To address this challenge, this study proposes an automated sentiment analysis system using deep learning techniques to classify hotel reviews into positive and negative sentiments.The research utilizes a large-scale dataset comprising over 500,000 hotel reviews, which undergoes extensive preprocessing, including text cleaning, tokenization, stopword removal, and data balancing to ensure model reliability. Exploratory Data Analysis (EDA) is conducted to understand data distribution and extract meaningful patterns. The processed textual data is then transformed into numerical representations using tokenization and sequence padding techniques.Two deep learning models, Long Short-Term Memory (LSTM) and Bidirectional Long ShortTerm Memory (BiLSTM), are implemented to capture sequential dependencies and contextual relationships. 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 112 pp. Englisch.

Paperback. Condizione: new. Paperback. W erze cyfrowej gwaltowny rozwoj platform internetowych znaczaco zmienil branze hotelarska, w ktorej na decyzje klientow coraz wiekszy wplyw maja recenzje tworzone przez uzytkownikow. Recenzje te dostarczaja cennych informacji na temat doswiadczen klientow; jednak ogromna ilosc nieustrukturyzowanych danych tekstowych sprawia, ze reczna analiza jest nieefektywna i niepraktyczna. Aby sprostac temu wyzwaniu, w niniejszym badaniu zaproponowano zautomatyzowany system analizy sentymentu wykorzystujacy techniki glebokiego uczenia sie do klasyfikacji recenzji hoteli na pozytywne i negatywne. W badaniach wykorzystano zbiory danych na duza skale, obejmujace ponad 500 000 recenzji hoteli, ktore zostaly poddane szeroko zakrojonemu przetwarzaniu wstepnemu, w tym czyszczeniu tekstu, tokenizacji, usuwaniu slow zbednych oraz rownowazeniu danych w celu zapewnienia niezawodnosci modelu. Przeprowadzono eksploracyjna analize danych (EDA) w celu zrozumienia rozkladu danych i wydobycia znaczacych wzorcow. Przetworzone dane tekstowe sa nastepnie przeksztalcane na reprezentacje liczbowe przy uzyciu technik tokenizacji i wypelniania sekwencji. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Paperback. Condizione: new. Paperback. Im digitalen Zeitalter hat das rasante Wachstum von Online-Plattformen die Hotellerie erheblich veraendert, wobei Kundenentscheidungen zunehmend von nutzergenerierten Bewertungen beeinflusst werden. Diese Bewertungen liefern wertvolle Einblicke in die Kundenerfahrungen; allerdings macht die enorme Menge an unstrukturierten Textdaten eine manuelle Analyse ineffizient und unpraktisch. Um dieser Herausforderung zu begegnen, schlaegt diese Studie ein automatisiertes System zur Sentimentanalyse vor, das Deep-Learning-Techniken nutzt, um Hotelbewertungen in positive und negative Stimmungen zu klassifizieren. Die Untersuchung stuetzt sich auf einen umfangreichen Datensatz mit ueber 500.000 Hotelbewertungen, der einer umfassenden Vorverarbeitung unterzogen wird, einschliesslich Textbereinigung, Tokenisierung, Entfernung von Stoppwoertern und Datenausgleich, um die Zuverlaessigkeit des Modells sicherzustellen. Es wird eine explorative Datenanalyse (EDA) durchgefuehrt, um die Datenverteilung zu verstehen und aussagekraeftige Muster zu extrahieren. Die aufbereiteten Textdaten werden anschliessend mithilfe von Tokenisierung und Sequenz-Padding-Techniken in numerische Darstellungen umgewandelt. Zwei Deep-Learning-Modelle, Long Short-Term Memory (LSTM) und Bidirectional Long Short-Term Memory (BiLSTM), werden implementiert, um sequenzielle Abhaengigkeiten und kontextuelle Beziehungen zu erfassen. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Na era digital, o rapido crescimento das plataformas online transformou significativamente o setor hoteleiro, onde as decisoes dos clientes sao cada vez mais influenciadas pelas avaliacoes geradas pelos utilizadores. Estas avaliacoes fornecem informacoes valiosas sobre as experiencias dos clientes; no entanto, o vasto volume de dados textuais nao estruturados torna a analise manual ineficiente e impraticavel. Para dar resposta a este desafio, este estudo propoe um sistema automatizado de analise de sentimentos que utiliza tecnicas de aprendizagem profunda para classificar as avaliacoes de hoteis em sentimentos positivos e negativos. A investigacao utiliza um conjunto de dados em grande escala, composto por mais de 500 000 avaliacoes de hoteis, que e submetido a um pre-processamento exaustivo, incluindo limpeza de texto, tokenizacao, remocao de palavras irrelevantes e equilibrio de dados, para garantir a fiabilidade do modelo. E realizada uma Analise Exploratoria de Dados (EDA) para compreender a distribuicao dos dados e extrair padroes significativos. Os dados textuais processados sao entao transformados em representacoes numericas utilizando tecnicas de tokenizacao e preenchimento de sequencias. Sao implementados dois modelos de aprendizagem profunda, a Memoria de Curto e Longo Prazo (LSTM) e a Memoria de Curto e Longo Prazo Bidirecional (BiLSTM), para captar dependencias sequenciais e relacoes contextuais. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Nell'era digitale, la rapida crescita delle piattaforme online ha trasformato in modo significativo il settore dell'ospitalita, dove le decisioni dei clienti sono sempre piu influenzate dalle recensioni generate dagli utenti. Queste recensioni forniscono preziose informazioni sulle esperienze dei clienti; tuttavia, l'enorme volume di dati testuali non strutturati rende l'analisi manuale inefficiente e poco pratica. Per affrontare questa sfida, il presente studio propone un sistema automatizzato di analisi del sentiment che utilizza tecniche di deep learning per classificare le recensioni degli hotel in sentiment positivi e negativi. La ricerca utilizza un set di dati su larga scala composto da oltre 500.000 recensioni di hotel, che viene sottoposto a un'approfondita pre-elaborazione, comprendente la pulizia del testo, la tokenizzazione, la rimozione delle parole vuote e il bilanciamento dei dati per garantire l'affidabilita del modello. Viene condotta un'analisi esplorativa dei dati (EDA) per comprendere la distribuzione dei dati ed estrarre modelli significativi. I dati testuali elaborati vengono quindi trasformati in rappresentazioni numeriche utilizzando tecniche di tokenizzazione e di riempimento delle sequenze. 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. Análisis de opiniones de hoteles mediante aprendizaje profundo con BiLSTM | Modelos LSTM y LSTM bidireccionales | Kadupu Durga Prasad (u. a.) | Taschenbuch | Spanisch | 2026 | Ediciones Nuestro Conocimiento | EAN 9786630335378 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…

Paperback. Condizione: new. Paperback. A l'ere du numerique, la croissance rapide des plateformes en ligne a profondement transforme le secteur de l'hotellerie, ou les decisions des clients sont de plus en plus influencees par les avis generes par les utilisateurs. Ces avis fournissent des informations precieuses sur les experiences des clients; cependant, le volume considerable de donnees textuelles non structurees rend l'analyse manuelle inefficace et peu pratique. Pour relever ce defi, cette etude propose un systeme automatise d'analyse des sentiments utilisant des techniques d'apprentissage profond afin de classer les avis sur les hotels en sentiments positifs et negatifs. La recherche s'appuie sur un ensemble de donnees a grande echelle comprenant plus de 500 000 avis sur des hotels, qui font l'objet d'un pretraitement approfondi, incluant le nettoyage du texte, la tokenisation, la suppression des mots vides et l'equilibrage des donnees afin de garantir la fiabilite du modele. Une analyse exploratoire des donnees (EDA) est realisee afin de comprendre la distribution des donnees et d'en extraire des tendances significatives. Les donnees textuelles traitees sont ensuite transformees en representations numeriques a l'aide de techniques de tokenisation et de remplissage de sequences. 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. Analyse des sentiments dans les avis sur les hôtels à l'aide du Deep Learning et du modèle BiLSTM | Modèles LSTM et LSTM bidirectionnels | Kadupu Durga Prasad (u. a.) | Taschenbuch | Französisch | 2026 | Editions Notre Savoir | EAN 9786630337938 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…