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Online reviews are becoming increasingly abundant, which makes them sometimes overwhelming for the users. To mitigate the problem of information overload, online retailers often proceed to display them according to their helpfulness to other users. In recent years, research has been aimed at finding efficient ways to automatically predict review helpfulness. This paper offers insight on both the most appropriate algorithm for the task of predicting review helpfulness in the specific context of class imbalance and high overlap of class features, and on the pre-processing techniques which can improve classifier performance in that context. To do so, it considers three classification algorithms: random forest, multinomial naive Bayes and linear support vector machine that uses stochastic gradient descent for learning.
It shows that : (1) none of the considered algorithm exhibit satisfying performance when facing imbalanced datasets and similar class features; (2) the use of linguistic pre-processing techniques results in marginal or no improvement; (3) the use of frequency-based pre- processing yields moderate improvement; (4) re-sampling techniques are highly efficient, especially Synthetic Minority Over-sampling TEchnique (SMOTE); (5) Overall, random forest combined with SMOTE shows the best performance in terms of precision, recall and F1-score.

Machine learning --- Review helpfulness --- Text classification --- Class imbalance --- Prediction --- Online customer review --- Sciences économiques & de gestion > Gestion des systèmes d'information

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The current Special Issue launched with the aim of further enlightening important CH areas, inviting researchers to submit original/featured multidisciplinary research works related to heritage crowdsourcing, documentation, management, authoring, storytelling, and dissemination. Audience engagement is considered very important at both sites of the CH production–consumption chain (i.e., push and pull ends). At the same time, sustainability factors are placed at the center of the envisioned analysis. A total of eleven (11) contributions were finally published within this Special Issue, enlightening various aspects of contemporary heritage strategies placed in today’s ubiquitous society. The finally published papers are related but not limited to the following multidisciplinary topics:Digital storytelling for cultural heritage;Audience engagement in cultural heritage;Sustainability impact indicators of cultural heritage;Cultural heritage digitization, organization, and management;Collaborative cultural heritage archiving, dissemination, and management;Cultural heritage communication and education for sustainable development;Semantic services of cultural heritage;Big data of cultural heritage;Smart systems for Historical cities – smart cities;Smart systems for cultural heritage sustainability.

Film, TV & radio --- 3D modeling --- 3D reconstruction --- event detection --- Twitter --- spectral clustering --- cultural heritage --- social media --- news --- journalism --- semantic analysis --- big data --- data center --- digital marketing --- eco-friendly --- environmental communication --- green websites --- green culture --- green hosting --- sustainability --- software sustainability --- multimedia tools --- static analysis --- evolution analytics --- interactive documentary --- audience engagement --- digital storytelling --- intangible heritage --- media users' engagement --- marine heritage --- biocultural heritage --- heritage management --- heritage communication --- digital narrative --- Instagram --- UNESCO --- marine protected areas of outstanding universal value --- soundscapes --- audiovisual heritage --- semantic audio --- data-driven storytelling --- content crowdsourcing --- requirements engineering --- authoring tools --- 3D content --- IEEE 830 standard --- semantic indexing --- text classification --- Greek literature --- TextRank --- BERT --- smart cities --- energy transition --- Évora --- POCITYF --- relation extraction --- distant supervision --- deep neural networks --- Transformers --- Greek NLP --- literary fiction --- metadata extraction --- Katharevousa --- 3D modeling --- 3D reconstruction --- event detection --- Twitter --- spectral clustering --- cultural heritage --- social media --- news --- journalism --- semantic analysis --- big data --- data center --- digital marketing --- eco-friendly --- environmental communication --- green websites --- green culture --- green hosting --- sustainability --- software sustainability --- multimedia tools --- static analysis --- evolution analytics --- interactive documentary --- audience engagement --- digital storytelling --- intangible heritage --- media users' engagement --- marine heritage --- biocultural heritage --- heritage management --- heritage communication --- digital narrative --- Instagram --- UNESCO --- marine protected areas of outstanding universal value --- soundscapes --- audiovisual heritage --- semantic audio --- data-driven storytelling --- content crowdsourcing --- requirements engineering --- authoring tools --- 3D content --- IEEE 830 standard --- semantic indexing --- text classification --- Greek literature --- TextRank --- BERT --- smart cities --- energy transition --- Évora --- POCITYF --- relation extraction --- distant supervision --- deep neural networks --- Transformers --- Greek NLP --- literary fiction --- metadata extraction --- Katharevousa

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The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved state-of-the-art applications in many fields, such as computer vision, speech and text processing, medical applications, and IoT (Internet of Things). The probability of a successful outcome from a neural network is linked to selection of an appropriate network architecture and training algorithm. Accordingly, much of the recent research on neural networks has been devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. This book gives significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications.

Information technology industries --- facial image analysis --- facial nerve paralysis --- deep convolutional neural networks --- image classification --- Chinese text classification --- long short-term memory --- convolutional neural network --- Arabic named entity recognition --- bidirectional recurrent neural network --- GRU --- LSTM --- natural language processing --- word embedding --- CNN --- object detection network --- attention mechanism --- feature fusion --- LSTM-CRF model --- elements recognition --- linguistic features --- POS syntactic rules --- action recognition --- fused features --- 3D convolution neural network --- motion map --- long short-term-memory --- tooth-marked tongue --- gradient-weighted class activation maps --- ship identification --- fully convolutional network --- embedded deep learning --- scalability --- gesture recognition --- human computer interaction --- alternative fusion neural network --- deep learning --- sentiment attention mechanism --- bidirectional gated recurrent unit --- Internet of Things --- convolutional neural networks --- graph partitioning --- distributed systems --- resource-efficient inference --- pedestrian attribute recognition --- graph convolutional network --- multi-label learning --- autoencoders --- long-short-term memory networks --- convolution neural Networks --- object recognition --- sentiment analysis --- text recognition --- IoT (Internet of Thing) systems --- medical applications

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The current Special Issue launched with the aim of further enlightening important CH areas, inviting researchers to submit original/featured multidisciplinary research works related to heritage crowdsourcing, documentation, management, authoring, storytelling, and dissemination. Audience engagement is considered very important at both sites of the CH production–consumption chain (i.e., push and pull ends). At the same time, sustainability factors are placed at the center of the envisioned analysis. A total of eleven (11) contributions were finally published within this Special Issue, enlightening various aspects of contemporary heritage strategies placed in today’s ubiquitous society. The finally published papers are related but not limited to the following multidisciplinary topics:Digital storytelling for cultural heritage;Audience engagement in cultural heritage;Sustainability impact indicators of cultural heritage;Cultural heritage digitization, organization, and management;Collaborative cultural heritage archiving, dissemination, and management;Cultural heritage communication and education for sustainable development;Semantic services of cultural heritage;Big data of cultural heritage;Smart systems for Historical cities – smart cities;Smart systems for cultural heritage sustainability.

3D modeling --- 3D reconstruction --- event detection --- Twitter --- spectral clustering --- cultural heritage --- social media --- news --- journalism --- semantic analysis --- big data --- data center --- digital marketing --- eco-friendly --- environmental communication --- green websites --- green culture --- green hosting --- sustainability --- software sustainability --- multimedia tools --- static analysis --- evolution analytics --- interactive documentary --- audience engagement --- digital storytelling --- intangible heritage --- media users’ engagement --- marine heritage --- biocultural heritage --- heritage management --- heritage communication --- digital narrative --- Instagram --- UNESCO --- marine protected areas of outstanding universal value --- soundscapes --- audiovisual heritage --- semantic audio --- data-driven storytelling --- content crowdsourcing --- requirements engineering --- authoring tools --- 3D content --- IEEE 830 standard --- semantic indexing --- text classification --- Greek literature --- TextRank --- BERT --- smart cities --- energy transition --- Évora --- POCITYF --- relation extraction --- distant supervision --- deep neural networks --- Transformers --- Greek NLP --- literary fiction --- metadata extraction --- Katharevousa --- n/a --- media users' engagement --- Évora

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The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved state-of-the-art applications in many fields, such as computer vision, speech and text processing, medical applications, and IoT (Internet of Things). The probability of a successful outcome from a neural network is linked to selection of an appropriate network architecture and training algorithm. Accordingly, much of the recent research on neural networks has been devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. This book gives significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications.

facial image analysis --- facial nerve paralysis --- deep convolutional neural networks --- image classification --- Chinese text classification --- long short-term memory --- convolutional neural network --- Arabic named entity recognition --- bidirectional recurrent neural network --- GRU --- LSTM --- natural language processing --- word embedding --- CNN --- object detection network --- attention mechanism --- feature fusion --- LSTM-CRF model --- elements recognition --- linguistic features --- POS syntactic rules --- action recognition --- fused features --- 3D convolution neural network --- motion map --- long short-term-memory --- tooth-marked tongue --- gradient-weighted class activation maps --- ship identification --- fully convolutional network --- embedded deep learning --- scalability --- gesture recognition --- human computer interaction --- alternative fusion neural network --- deep learning --- sentiment attention mechanism --- bidirectional gated recurrent unit --- Internet of Things --- convolutional neural networks --- graph partitioning --- distributed systems --- resource-efficient inference --- pedestrian attribute recognition --- graph convolutional network --- multi-label learning --- autoencoders --- long-short-term memory networks --- convolution neural Networks --- object recognition --- sentiment analysis --- text recognition --- IoT (Internet of Thing) systems --- medical applications