Integrating Grammatical Features into CNN Model for Emotion Classification

Huynh, Thi-Thanh-Thuy; Le, Anh-Cuong

doi:10.1109/nics.2018.8606875

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…Finally, the Fully Connected layer filters high-level representation of the input and converts them into votes. Fully connected is a way to connect layers in two layers together [6]. Fig.…”

Section: ( )mentioning

confidence: 99%

“…The word vectors shows semantics and their dimension is usually low, this transformation obtains to calculate the connection among words and dimensionality reduction for efficient representation [6,5]. The NN takes words from a vocabulary as input and then embeds them as vectors into a lower-dimensional space, which is denoted to as Embedding Layer [5].…”

Section: Seda Postalcioglu Senem Aktasmentioning

confidence: 99%

“…Layers [6]. Convolution layer extracts the features of the input by using a slide filter and applies the scalar product [6]. Feature map is the output of convolution [6].…”

Section: Cnn (Convolutional Neural Network) Contains Three Layers As the Convolutional Pooling And Full-connectedmentioning

confidence: 99%

“…Convolution layer extracts the features of the input by using a slide filter and applies the scalar product [6]. Feature map is the output of convolution [6]. Matrices obtained by convolution operators are transformed with an activation function.…”

Section: Cnn (Convolutional Neural Network) Contains Three Layers As the Convolutional Pooling And Full-connectedmentioning

confidence: 99%

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Comparison of Neural Network Models for Nostalgic Sentiment Analysis of YouTube Comments

Postalcioglu

Aktas

2020

Hittite J. Sci. Eng.

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W ith the rapid development of social networking sites, people need to explain their opinions and thoughts. People share not only information with websites but also they begin to express their thoughts and feelings more easily [1]. They expressed these posts by tweeting them on Twitter, posting on Instagram or commenting on social media. Based on this case, the feelings and thoughts of the interpreters can be analyzed [1]. Reference 11 can be shown as an example. It is possible to analyze such comments or posts according to some criteria such as what team hits, political opinion, positive/negative/neutral content of comments. This kind of analyzes may affect future production progress by enabling the public to learn the opinions, parties, and thoughts before the political election or to make a customer analysis for the products produced. It has a wide usage area. These analyses are carried out for many different purposes. With these studies, it is tried to predict the future. These analyses are referred to as "Sentiment Analysis" in terminology [1]. Sentiment analysis is one of the techniques commonly used in Natural Language Processing (NLP). NLP helps us to understand the content of a text [2]. There are different

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“…Finally, the Fully Connected layer filters high-level representation of the input and converts them into votes. Fully connected is a way to connect layers in two layers together [6]. Fig.…”

Section: ( )mentioning

confidence: 99%

Section: Seda Postalcioglu Senem Aktasmentioning

confidence: 99%

“…Layers [6]. Convolution layer extracts the features of the input by using a slide filter and applies the scalar product [6]. Feature map is the output of convolution [6].…”

Section: Cnn (Convolutional Neural Network) Contains Three Layers As the Convolutional Pooling And Full-connectedmentioning

confidence: 99%

Section: Cnn (Convolutional Neural Network) Contains Three Layers As the Convolutional Pooling And Full-connectedmentioning

confidence: 99%

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Comparison of Neural Network Models for Nostalgic Sentiment Analysis of YouTube Comments

Postalcioglu

Aktas

2020

Hittite J. Sci. Eng.

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“…with accuracy of about 90-95% [9,10]. In general, the recognition of Vietnamese text has been a matter of concern for many scientific researchers based on the different models and techniques such as using HANDS-VNOnDB [11], utilizing RetinaNet for text detection and Inception-v3 CNN network for feature extraction, passing through bidirectional long short-term memory (Bi-LSTM)-based RNN for text recognition [12], Bi-LSTM combined with Conditional Random Field (CRF) [13], SSD Mobilenet V2 for text detection and Attention OCR (AOCR) for text recognition [14], CNN, a well-known for image processing applications [15][16][17][18], integrated with grammatical features for emotion detection [16].…”

Section: Introductionmentioning

confidence: 99%

Vietnamese character recognition based on CNN model with reduced character classes

2021

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This article will detail the steps to build and train the convolutional neural network (CNN) model for Vietnamese character recognition in educational books. Based on this model, a mobile application for extracting text content from images in Vietnamese textbooks was built using OpenCV and Canny edge detection algorithm. There are 178 characters classes in Vietnamese with accents. However, within the scope of Vietnamese character recognition in textbooks, some classes of characters only differ in terms of actual sizes, such as “c” and “C”, “o” and “O”. Therefore, the authors built the classification model for 138 Vietnamese character classes after filtering out similar character classes to increase the model's effectiveness.

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Sentiment analysis versus aspect-based sentiment analysis versus emotion analysis from text: a comparative study

Shukla,

Dwivedi

2024

Int J Syst Assur Eng Manag

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Integrating Grammatical Features into CNN Model for Emotion Classification

Cited by 5 publications

References 20 publications

Comparison of Neural Network Models for Nostalgic Sentiment Analysis of YouTube Comments

Comparison of Neural Network Models for Nostalgic Sentiment Analysis of YouTube Comments

Vietnamese character recognition based on CNN model with reduced character classes

Sentiment analysis versus aspect-based sentiment analysis versus emotion analysis from text: a comparative study

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