Multimodal Sentiment Analysis

TRAMA

This latest volume in the series, Socio-Affective Computing, presents a set of novel approaches to analyze opinionated videos and to extract sentiments and emotions.  Textual sentiment analysis framework as discussed in this book contains a novel way of doing sentiment analysis by merging linguistics with machine learning. Fusing textual information with audio and visual cues is found to be extremely useful which improves text, audio and visual based unimodal sentiment analyzer. This volume covers the three main topics of: textual preprocessing and sentiment analysis methods; frameworks to process audio and visual data; and methods of textual, audio and visual features fusion. The inclusion of key visualization and case studies will enable readers to understand better these approaches.  Aimed at the Natural Language Processing, Affective Computing and Artificial Intelligence audiences, this comprehensive volume will appeal to a wide readership and will help readers to understand key details on multimodal sentiment analysis.

SOMMARIO

Preface  1 Introduction and MotivationResearch Challenges in Text-Based Sentiment AnalysisResearch Challenges in Multimodal Sentiment AnalysisOverview of the Proposed FrameworkContributions of this BookBook Organisation  2 BackgroundAffective ComputingSentiment AnalysisPattern Recognition  Feature SelectionModel Evaluation Techniques  Model Validation TechniquesClassification TechniquesFeature-Based Text RepresentationConclusion  3 Literature Survey and DatasetsIntroductionAvailable DatasetsVisual, Audio Features for Affect RecognitionMultimodal Affect RecognitionAvailable APIsDiscussionConclusion  4 Concept Extraction from Natural Text for Concept Level Text AnalysisIntroductionThe patterns for concept extractionExperiments and ResultsConclusion  5 EmoSenticSpace: Dense concept-based affective features with common-sense knowledgeIntroductionLexical Resources UsedFeatures Used for ClassificationFuzzy ClusteringHard ClusteringImplementationDirect Evaluation Of The Assigned Emotion LabelsConstruction Of EmosenticspacePerformance on ApplicationsSummary of Lexical Resources and Features UsedConclusion  6 Sentic Patterns: Sentiment Data Flow Analysis by Means of Dynamic Linguistic PatternsIntroductionGeneral rulesCombining sentic patterns with machine learning for text-basedsentiment analysisEvaluationConclusion  7 Combining Textual Clues with Audio-Visual Information for Multimodal Sentiment AnalysisIntroductionExtracting Features from Textual DataExtracting Features from Visual DataExtracting Features from Audio DataExperimental ResultsSpeeding up the computational time: The role of ELMImproved multimodal sentiment analysis: Deep learning-basedvisual feature extractionConvolutional Recurrent Multiple Kernel Learning (CRMKL)Experimental Results and DiscussionConclusion  8 Conclusion and Future WorkSocial ImpactAdvantagesLimitationsFuture Work Index

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9783319950181
• Collana: Socio-Affective Computing
• Dimensioni: 235 x 155 mm Ø 593 gr
• Formato: Copertina rigida
• Illustration Notes: XI, 214 p. 34 illus., 25 illus. in color.
• Pagine Arabe: 214
• Pagine Romane: xi