Deep Neural Networks for Text, Image, and Video Sentiment Analysis

The rapid growth of digital content across social media, online platforms, multimedia repositories, and communication systems has created an increasing need for automated methods capable of understanding sentiment across different data modalities. Deep Neural Networks for Text, Image, and Video Sentiment Analysis provides a focused technical examination of multimodal sentiment analysis, deep neural networks, natural language processing, computer vision, video analysis, and machine learning. The book brings together computational approaches for analyzing sentiment expressed through textual, visual, and video content.

The book introduces the foundations of sentiment analysis and examines how computational methods can identify opinions, emotions, attitudes, and sentiment-related patterns within digital information. While traditional sentiment analysis has primarily focused on textual data, modern digital communication increasingly combines text with images and video. This multimodal environment creates additional challenges because each data type has different structures, representations, and information characteristics.

A central focus is placed on deep neural networks for sentiment classification and prediction. Deep learning models can learn hierarchical representations from large and complex datasets, reducing the need for extensive manual feature engineering. The book discusses general concepts related to data preparation, feature extraction, representation learning, model architecture, training, validation, classification, and performance evaluation.

Text-based sentiment analysis is examined through the perspective of natural language processing. Text data may contain contextual information, linguistic patterns, expressions of opinion, and emotional cues. The book considers computational approaches for preprocessing textual information and representing it in forms suitable for deep learning models. Classification approaches can then be applied to distinguish sentiment categories such as positive, negative, or neutral responses.

Image sentiment analysis introduces an additional dimension by examining visual information. Images can communicate emotions and opinions through objects, scenes, facial expressions, colors, composition, and contextual elements. Deep neural networks, particularly image-oriented architectures, can learn visual representations that may be useful for sentiment classification. The book discusses the general principles of image preprocessing, feature learning, classification, and evaluation.