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WHAT MEN SAY, WHAT WOMEN HEAR: FINDING GENDER- SPECIFI C computer, , , , , , , , 16 A HYBRID APPROACH FOR SENTIMENT ANALYSIS APPLIED TO PAPER A Hybrid Approach for Sentiment Analysis Applied to Paper Reviews KDD’17, August 2017, Halifax, Nova Scotia, Canada An e‡ective sentiment analysis requires not only considering UNDERSTANDING FILTER BUBBLES AND POLARIZATION IN SOCIAL Understanding Filter Bubbles and Polarization in Social Networks WISDOM’19, August 4, 2019, Anchorage, Alaska Other opinion dynamics models and metrics have also been used DOWNLOADS « SENTICNET The latest version of the knowledge base can also be downloaded in OWL format as an ontology and selectively accessed online through an API, available in 40 different languages. Each language-dependent version of SenticNet is also downloadable in RDF/XML format as BabelSenticNet. SenticNet is also available in the form of embeddings as PROJECTS « SENTICNET At SenticNet, we are working on several projects spanning from fundamental affective computing research to the application of sentiment analysis techniques to domains like finance, healthcare, and the arts. Each project leverages the specific expertise of one or more members of the Sentic Team but, in fact, all projects are highly RECENT TRENDS IN DEEP LEARNING BASED PERSONALITY DETECTION 2318 Y.Mehtaetal. from text, non-verbal communication (e.g., interpersonal distances, speech and body move-ments), social media, mobiles, wearable devices and finally from computer games as well. MEMORY FUSION NETWORK FOR MULTI-VIEW SEQUENTIAL LEARNING ˇˆ˙˝˛ Delta-memory Attention Network Multi-view Gated Memory System of LSTMs ˚ ˚ ˜ ˆ˙˝ ˆ ˆ ˝ ˆ ! ˆ " ˆ # Figure 1: Overview figure of Memory Fusion Network (MFN) pipeline. σdenotes the sigmoidactivation function, τthe tanh activation function, ⊙the Hadamard product and ⊕element wise addition. TARGETED ASPECT-BASED SENTIMENT ANALYSIS VIA EMBEDDING Task Definition A sentence s consists of a sequence of words. Similar to (Wang et al. 2017), we consider all mentions of the same target as a single target. A target tcomposed of mwords in sentence s, denoted as T ={t1,t2,⋯,ti,⋯,tm}with ti re- ferring to the position of ith word in the target expression, the task of targeted ABSA can be divided into two subtasks. ENSEMBLE APPLICATION OF CONVOLUTIONAL AND RECURRENT NEURAL Ensemble Application of Convolutional and Recurrent Neural Networks for Multi-label Text Categorization Guibin Chen 1, Deheng Ye , Zhenchang Xing2, Jieshan Chen3, Erik Cambria 1 School of Computer Science and Engineering, Nanyang Technological University, Singapore 2 Research School of Computer Science, Australian National University, Australia 3 School of Mathematics, Sun Yat-sen University TENSOR FUSION NETWORK FOR MULTIMODAL SENTIMENT ANALYSIS Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1114–1125 Copenhagen, Denmark, September7–11, 2017.
WHAT MEN SAY, WHAT WOMEN HEAR: FINDING GENDER- SPECIFI C computer, , , , , , , , 16 A HYBRID APPROACH FOR SENTIMENT ANALYSIS APPLIED TO PAPER A Hybrid Approach for Sentiment Analysis Applied to Paper Reviews KDD’17, August 2017, Halifax, Nova Scotia, Canada An e‡ective sentiment analysis requires not only considering UNDERSTANDING FILTER BUBBLES AND POLARIZATION IN SOCIAL Understanding Filter Bubbles and Polarization in Social Networks WISDOM’19, August 4, 2019, Anchorage, Alaska Other opinion dynamics models and metrics have also been usedSENTICNET
1) a concept-level knowledge base; 2) a multi-disciplinary framework; 3) a private company. As a knowledge base, SenticNet provides a set of semantics, sentics, and polarity associated with 200,000 natural language concepts. In particular, semantics define the denotative information associated with words and multiword expressions (i.e MEMORY FUSION NETWORK FOR MULTI-VIEW SEQUENTIAL LEARNING ˇˆ˙˝˛ Delta-memory Attention Network Multi-view Gated Memory System of LSTMs ˚ ˚ ˜ ˆ˙˝ ˆ ˆ ˝ ˆ ! ˆ " ˆ # Figure 1: Overview figure of Memory Fusion Network (MFN) pipeline. σdenotes the sigmoidactivation function, τthe tanh activation function, ⊙the Hadamard product and ⊕element wise addition. MULTI-ATTENTION RECURRENT NETWORK FOR HUMAN COMMUNICATION Multi-Attention Block Figure 1: Overview of Multi-attention Recurrent Network (MARN) with Long-short Term Hybrid Memory (LSTHM) and Multi-attention Block (MAB) components, for M = {l,v,a}representing the language, vision and acousticmodalities respectively. MULTIMODAL SENTIMENT ANALYSIS USING HIERARCHICAL FUSION apply a deep Convolutional Neural Networks (CNN) on each ut-terance to extract textual features. Each utterance in the text isre-presented as an
ANAPHORA AND COREFERENCE RESOLUTION: A REVIEW R. Sukthanker, S. Poria and E. Cambria et al. Information Fusion 59 (2020) 139–162 3.1. Zero anaphora This type of anaphora is particularly common in prose and ornamen- DEEP LEARNING-BASED DOCUMENT MODELING FOR PERSONALITY features Personality traits WHAT MEN SAY, WHAT WOMEN HEAR: FINDING GENDER- SPECIFI C computer, , , , , , , , 16 A HYBRID APPROACH FOR SENTIMENT ANALYSIS APPLIED TO PAPER A Hybrid Approach for Sentiment Analysis Applied to Paper Reviews KDD’17, August 2017, Halifax, Nova Scotia, Canada An e‡ective sentiment analysis requires not only considering A DECADE OF SENTIC COMPUTING A Decade of Sentic Computing 5 piled gold standard data, and (ii) a qualitative evaluation of a domain-speci c a ective model for television programme brands. PHONETIC-BASED MICROTEXT NORMALIZATION FOR TWITTER Phonetic-Based Microtext Normalization for Twitter Sentiment Analysis Ranjan Satapathy, Claudia Guerreiro, Iti Chaturvedi, Erik Cambria Nanyang Technological University DOWNLOADS « SENTICNET The latest version of the knowledge base can also be downloaded in OWL format as an ontology and selectively accessed online through an API, available in 40 different languages. Each language-dependent version of SenticNet is also downloadable in RDF/XML format as BabelSenticNet. SenticNet is also available in the form of embeddings as PROJECTS « SENTICNET At SenticNet, we are working on several projects spanning from fundamental affective computing research to the application of sentiment analysis techniques to domains like finance, healthcare, and the arts. Each project leverages the specific expertise of one or more members of the Sentic Team but, in fact, all projects are highly MULTI-ATTENTION RECURRENT NETWORK FOR HUMAN COMMUNICATION Multi-Attention Block Figure 1: Overview of Multi-attention Recurrent Network (MARN) with Long-short Term Hybrid Memory (LSTHM) and Multi-attention Block (MAB) components, for M = {l,v,a}representing the language, vision and acousticmodalities respectively. SENTIC PATTERNS: DEPENDENCY-BASED RULES FOR CONCEPT-LEVEL Sentic patterns: Dependency-based rules for concept-level sentiment analysis Soujanya Poriaa, Erik Cambriab,⇑, Grégoire Wintersteinc, Guang-Bin Huanga a School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore bSchool of Computer Engineering, Nanyang Technological University, Singapore cDepartment of Linguistics, University of Paris Diderot, France RECENT TRENDS IN DEEP LEARNING BASED PERSONALITY DETECTION 2318 Y.Mehtaetal. from text, non-verbal communication (e.g., interpersonal distances, speech and body move-ments), social media, mobiles, wearable devices and finally from computer games as well. MEMORY FUSION NETWORK FOR MULTI-VIEW SEQUENTIAL LEARNING ˇˆ˙˝˛ Delta-memory Attention Network Multi-view Gated Memory System of LSTMs ˚ ˚ ˜ ˆ˙˝ ˆ ˆ ˝ ˆ ! ˆ " ˆ # Figure 1: Overview figure of Memory Fusion Network (MFN) pipeline. σdenotes the sigmoidactivation function, τthe tanh activation function, ⊙the Hadamard product and ⊕element wise addition. MULTIMODAL SENTIMENT ANALYSIS USING HIERARCHICAL FUSION apply a deep Convolutional Neural Networks (CNN) on each ut-terance to extract textual features. Each utterance in the text isre-presented as an
ENSEMBLE APPLICATION OF CONVOLUTIONAL AND RECURRENT NEURAL Ensemble Application of Convolutional and Recurrent Neural Networks for Multi-label Text Categorization Guibin Chen 1, Deheng Ye , Zhenchang Xing2, Jieshan Chen3, Erik Cambria 1 School of Computer Science and Engineering, Nanyang Technological University, Singapore 2 Research School of Computer Science, Australian National University, Australia 3 School of Mathematics, Sun Yat-sen University UNDERSTANDING FILTER BUBBLES AND POLARIZATION IN SOCIAL Understanding Filter Bubbles and Polarization in Social Networks WISDOM’19, August 4, 2019, Anchorage, Alaska Other opinion dynamics models and metrics have also been used PHONETIC-BASED MICROTEXT NORMALIZATION FOR TWITTER Phonetic-Based Microtext Normalization for Twitter Sentiment Analysis Ranjan Satapathy, Claudia Guerreiro, Iti Chaturvedi, Erik Cambria Nanyang Technological University DOWNLOADS « SENTICNET The latest version of the knowledge base can also be downloaded in OWL format as an ontology and selectively accessed online through an API, available in 40 different languages. Each language-dependent version of SenticNet is also downloadable in RDF/XML format as BabelSenticNet. SenticNet is also available in the form of embeddings as PROJECTS « SENTICNET At SenticNet, we are working on several projects spanning from fundamental affective computing research to the application of sentiment analysis techniques to domains like finance, healthcare, and the arts. Each project leverages the specific expertise of one or more members of the Sentic Team but, in fact, all projects are highly MULTI-ATTENTION RECURRENT NETWORK FOR HUMAN COMMUNICATION Multi-Attention Block Figure 1: Overview of Multi-attention Recurrent Network (MARN) with Long-short Term Hybrid Memory (LSTHM) and Multi-attention Block (MAB) components, for M = {l,v,a}representing the language, vision and acousticmodalities respectively. SENTIC PATTERNS: DEPENDENCY-BASED RULES FOR CONCEPT-LEVEL Sentic patterns: Dependency-based rules for concept-level sentiment analysis Soujanya Poriaa, Erik Cambriab,⇑, Grégoire Wintersteinc, Guang-Bin Huanga a School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore bSchool of Computer Engineering, Nanyang Technological University, Singapore cDepartment of Linguistics, University of Paris Diderot, France RECENT TRENDS IN DEEP LEARNING BASED PERSONALITY DETECTION 2318 Y.Mehtaetal. from text, non-verbal communication (e.g., interpersonal distances, speech and body move-ments), social media, mobiles, wearable devices and finally from computer games as well. MEMORY FUSION NETWORK FOR MULTI-VIEW SEQUENTIAL LEARNING ˇˆ˙˝˛ Delta-memory Attention Network Multi-view Gated Memory System of LSTMs ˚ ˚ ˜ ˆ˙˝ ˆ ˆ ˝ ˆ ! ˆ " ˆ # Figure 1: Overview figure of Memory Fusion Network (MFN) pipeline. σdenotes the sigmoidactivation function, τthe tanh activation function, ⊙the Hadamard product and ⊕element wise addition. MULTIMODAL SENTIMENT ANALYSIS USING HIERARCHICAL FUSION apply a deep Convolutional Neural Networks (CNN) on each ut-terance to extract textual features. Each utterance in the text isre-presented as an
ENSEMBLE APPLICATION OF CONVOLUTIONAL AND RECURRENT NEURAL Ensemble Application of Convolutional and Recurrent Neural Networks for Multi-label Text Categorization Guibin Chen 1, Deheng Ye , Zhenchang Xing2, Jieshan Chen3, Erik Cambria 1 School of Computer Science and Engineering, Nanyang Technological University, Singapore 2 Research School of Computer Science, Australian National University, Australia 3 School of Mathematics, Sun Yat-sen University UNDERSTANDING FILTER BUBBLES AND POLARIZATION IN SOCIAL Understanding Filter Bubbles and Polarization in Social Networks WISDOM’19, August 4, 2019, Anchorage, Alaska Other opinion dynamics models and metrics have also been used PHONETIC-BASED MICROTEXT NORMALIZATION FOR TWITTER Phonetic-Based Microtext Normalization for Twitter Sentiment Analysis Ranjan Satapathy, Claudia Guerreiro, Iti Chaturvedi, Erik Cambria Nanyang Technological UniversityAPI - SENTICNET
Sentic API. Sentic API provides an easy and intuitive way to access SenticNet 6, a semantic network of commonsense knowledge that contains 200,000 nodes (words and multiword expressions) and thousands of connections (relationships between nodes), which is also available in different formats on our downloads page. Please note that Sentic API does not perform sentence-level sentiment analysis MEMORY FUSION NETWORK FOR MULTI-VIEW SEQUENTIAL LEARNING ˇˆ˙˝˛ Delta-memory Attention Network Multi-view Gated Memory System of LSTMs ˚ ˚ ˜ ˆ˙˝ ˆ ˆ ˝ ˆ ! ˆ " ˆ # Figure 1: Overview figure of Memory Fusion Network (MFN) pipeline. σdenotes the sigmoidactivation function, τthe tanh activation function, ⊙the Hadamard product and ⊕element wise addition. LEARNING COMMUNITY EMBEDDINGWITH COMMUNITY DETECTION AND Community Detection Community Embedding Node Embedding J/ J. J0 Figure 2: A closed loop for learning community embedding. Because each community is a group of densely connected nodes, RADICAL-BASED HIERARCHICAL EMBEDDINGS FOR CHINESE Radical-Based Hierarchical Embeddings for Chinese Sentiment Analysis at Sentence Level Haiyun Peng, Erik Cambria School of Computer Scienceand Engineering
WEAKLY SUPERVISED SEMANTIC SEGMENTATION WITH … WEAKLY SUPERVISED SEMANTIC SEGMENTATION WITH SUPERPIXEL EMBEDDING Frank Z. Xing 1, Erik Cambria , Win-Bin Huang2 and Yang Xu2 1 School of Computer Science and Engineering, Nanyang Technological University, Singapore 2 Department of Information Management, Peking University, China ABSTRACT In this paper, we propose to use contexts ofsuperpixels as a
ONE BELT, ONE ROAD, ONE SENTIMENT? A HYBRID APPROACH TO A literature review on this issue has highlighted Environ-ment, Energy, Project Operations, Economics and Trade as key themes in academic literature . A DEEPER LOOK INTO SARCASTIC TWEETS USING DEEP Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1601–1612, Osaka, Japan, December 11-17 2016.SENTIC
Sentic
SENTIMENT EXTRACTION FROM CONSUMER-GENERATED NOISY SHORT TEXTS Sentiment extraction from Consumer-generated noisy short texts Hardik Meisheri TCS Innovation Labs New Delhi, India hardik.meisheri@tcs.comKunal Ranjan
JUMPING NLP CURVES: A REVIEW OF NATURAL LANGUAGE 50 IEEE COMPUTAT IONAL NT ELL G NCE MAGAZ | MAY 2014 Each rule is independent from the oth-ers, allowing rules to be added and deleted easily. Production rule systems have a DOWNLOADS « SENTICNET The latest version of the knowledge base can also be downloaded in OWL format as an ontology and selectively accessed online through an API, available in 40 different languages. Each language-dependent version of SenticNet is also downloadable in RDF/XML format as BabelSenticNet. SenticNet is also available in the form of embeddings as PROJECTS « SENTICNET At SenticNet, we are working on several projects spanning from fundamental affective computing research to the application of sentiment analysis techniques to domains like finance, healthcare, and the arts. Each project leverages the specific expertise of one or more members of the Sentic Team but, in fact, all projects are highly MULTI-ATTENTION RECURRENT NETWORK FOR HUMAN COMMUNICATION Multi-Attention Block Figure 1: Overview of Multi-attention Recurrent Network (MARN) with Long-short Term Hybrid Memory (LSTHM) and Multi-attention Block (MAB) components, for M = {l,v,a}representing the language, vision and acousticmodalities respectively. SENTIC PATTERNS: DEPENDENCY-BASED RULES FOR CONCEPT-LEVEL Sentic patterns: Dependency-based rules for concept-level sentiment analysis Soujanya Poriaa, Erik Cambriab,⇑, Grégoire Wintersteinc, Guang-Bin Huanga a School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore bSchool of Computer Engineering, Nanyang Technological University, Singapore cDepartment of Linguistics, University of Paris Diderot, France RECENT TRENDS IN DEEP LEARNING BASED PERSONALITY DETECTION 2318 Y.Mehtaetal. from text, non-verbal communication (e.g., interpersonal distances, speech and body move-ments), social media, mobiles, wearable devices and finally from computer games as well. MEMORY FUSION NETWORK FOR MULTI-VIEW SEQUENTIAL LEARNING ˇˆ˙˝˛ Delta-memory Attention Network Multi-view Gated Memory System of LSTMs ˚ ˚ ˜ ˆ˙˝ ˆ ˆ ˝ ˆ ! ˆ " ˆ # Figure 1: Overview figure of Memory Fusion Network (MFN) pipeline. σdenotes the sigmoidactivation function, τthe tanh activation function, ⊙the Hadamard product and ⊕element wise addition. MULTIMODAL SENTIMENT ANALYSIS USING HIERARCHICAL FUSION apply a deep Convolutional Neural Networks (CNN) on each ut-terance to extract textual features. Each utterance in the text isre-presented as an
ENSEMBLE APPLICATION OF CONVOLUTIONAL AND RECURRENT NEURALCONVOLUTIONAL VS RECURRENT NEURAL NETWORKSDIFFUSION CONVOLUTIONAL RECURRENT NEURAL NET…HYPERGRAPH CONVOLUTIONAL RECURRENT NEURAL …CONVOLUTIONAL NEURAL NETWORK PAPER3D CONVOLUTIONAL NEURAL NETWORKCONVOLUTIONAL NEURAL NETWORK TENSORFLOW Ensemble Application of Convolutional and Recurrent Neural Networks for Multi-label Text Categorization Guibin Chen 1, Deheng Ye , Zhenchang Xing2, Jieshan Chen3, Erik Cambria 1 School of Computer Science and Engineering, Nanyang Technological University, Singapore 2 Research School of Computer Science, Australian National University, Australia 3 School of Mathematics, Sun Yat-sen University UNDERSTANDING FILTER BUBBLES AND POLARIZATION IN SOCIAL Understanding Filter Bubbles and Polarization in Social Networks WISDOM’19, August 4, 2019, Anchorage, Alaska Other opinion dynamics models and metrics have also been used PHONETIC-BASED MICROTEXT NORMALIZATION FOR TWITTER Phonetic-Based Microtext Normalization for Twitter Sentiment Analysis Ranjan Satapathy, Claudia Guerreiro, Iti Chaturvedi, Erik Cambria Nanyang Technological University DOWNLOADS « SENTICNET The latest version of the knowledge base can also be downloaded in OWL format as an ontology and selectively accessed online through an API, available in 40 different languages. Each language-dependent version of SenticNet is also downloadable in RDF/XML format as BabelSenticNet. SenticNet is also available in the form of embeddings as PROJECTS « SENTICNET At SenticNet, we are working on several projects spanning from fundamental affective computing research to the application of sentiment analysis techniques to domains like finance, healthcare, and the arts. Each project leverages the specific expertise of one or more members of the Sentic Team but, in fact, all projects are highly MULTI-ATTENTION RECURRENT NETWORK FOR HUMAN COMMUNICATION Multi-Attention Block Figure 1: Overview of Multi-attention Recurrent Network (MARN) with Long-short Term Hybrid Memory (LSTHM) and Multi-attention Block (MAB) components, for M = {l,v,a}representing the language, vision and acousticmodalities respectively. SENTIC PATTERNS: DEPENDENCY-BASED RULES FOR CONCEPT-LEVEL Sentic patterns: Dependency-based rules for concept-level sentiment analysis Soujanya Poriaa, Erik Cambriab,⇑, Grégoire Wintersteinc, Guang-Bin Huanga a School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore bSchool of Computer Engineering, Nanyang Technological University, Singapore cDepartment of Linguistics, University of Paris Diderot, France RECENT TRENDS IN DEEP LEARNING BASED PERSONALITY DETECTION 2318 Y.Mehtaetal. from text, non-verbal communication (e.g., interpersonal distances, speech and body move-ments), social media, mobiles, wearable devices and finally from computer games as well. MEMORY FUSION NETWORK FOR MULTI-VIEW SEQUENTIAL LEARNING ˇˆ˙˝˛ Delta-memory Attention Network Multi-view Gated Memory System of LSTMs ˚ ˚ ˜ ˆ˙˝ ˆ ˆ ˝ ˆ ! ˆ " ˆ # Figure 1: Overview figure of Memory Fusion Network (MFN) pipeline. σdenotes the sigmoidactivation function, τthe tanh activation function, ⊙the Hadamard product and ⊕element wise addition. MULTIMODAL SENTIMENT ANALYSIS USING HIERARCHICAL FUSION apply a deep Convolutional Neural Networks (CNN) on each ut-terance to extract textual features. Each utterance in the text isre-presented as an
ENSEMBLE APPLICATION OF CONVOLUTIONAL AND RECURRENT NEURALCONVOLUTIONAL VS RECURRENT NEURAL NETWORKSDIFFUSION CONVOLUTIONAL RECURRENT NEURAL NET…HYPERGRAPH CONVOLUTIONAL RECURRENT NEURAL …CONVOLUTIONAL NEURAL NETWORK PAPER3D CONVOLUTIONAL NEURAL NETWORKCONVOLUTIONAL NEURAL NETWORK TENSORFLOW Ensemble Application of Convolutional and Recurrent Neural Networks for Multi-label Text Categorization Guibin Chen 1, Deheng Ye , Zhenchang Xing2, Jieshan Chen3, Erik Cambria 1 School of Computer Science and Engineering, Nanyang Technological University, Singapore 2 Research School of Computer Science, Australian National University, Australia 3 School of Mathematics, Sun Yat-sen University UNDERSTANDING FILTER BUBBLES AND POLARIZATION IN SOCIAL Understanding Filter Bubbles and Polarization in Social Networks WISDOM’19, August 4, 2019, Anchorage, Alaska Other opinion dynamics models and metrics have also been used PHONETIC-BASED MICROTEXT NORMALIZATION FOR TWITTER Phonetic-Based Microtext Normalization for Twitter Sentiment Analysis Ranjan Satapathy, Claudia Guerreiro, Iti Chaturvedi, Erik Cambria Nanyang Technological UniversityAPI - SENTICNET
Sentic API. Sentic API provides an easy and intuitive way to access SenticNet 6, a semantic network of commonsense knowledge that contains 200,000 nodes (words and multiword expressions) and thousands of connections (relationships between nodes), which is also available in different formats on our downloads page. Please note that Sentic API does not perform sentence-level sentiment analysis MEMORY FUSION NETWORK FOR MULTI-VIEW SEQUENTIAL LEARNING ˇˆ˙˝˛ Delta-memory Attention Network Multi-view Gated Memory System of LSTMs ˚ ˚ ˜ ˆ˙˝ ˆ ˆ ˝ ˆ ! ˆ " ˆ # Figure 1: Overview figure of Memory Fusion Network (MFN) pipeline. σdenotes the sigmoidactivation function, τthe tanh activation function, ⊙the Hadamard product and ⊕element wise addition. LEARNING COMMUNITY EMBEDDINGWITH COMMUNITY DETECTION AND Community Detection Community Embedding Node Embedding J/ J. J0 Figure 2: A closed loop for learning community embedding. Because each community is a group of densely connected nodes, RADICAL-BASED HIERARCHICAL EMBEDDINGS FOR CHINESE Radical-Based Hierarchical Embeddings for Chinese Sentiment Analysis at Sentence Level Haiyun Peng, Erik Cambria School of Computer Scienceand Engineering
WEAKLY SUPERVISED SEMANTIC SEGMENTATION WITH … WEAKLY SUPERVISED SEMANTIC SEGMENTATION WITH SUPERPIXEL EMBEDDING Frank Z. Xing 1, Erik Cambria , Win-Bin Huang2 and Yang Xu2 1 School of Computer Science and Engineering, Nanyang Technological University, Singapore 2 Department of Information Management, Peking University, China ABSTRACT In this paper, we propose to use contexts ofsuperpixels as a
ONE BELT, ONE ROAD, ONE SENTIMENT? A HYBRID APPROACH TO A literature review on this issue has highlighted Environ-ment, Energy, Project Operations, Economics and Trade as key themes in academic literature . A DEEPER LOOK INTO SARCASTIC TWEETS USING DEEP Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1601–1612, Osaka, Japan, December 11-17 2016.SENTIC
Sentic
SENTIMENT EXTRACTION FROM CONSUMER-GENERATED NOISY SHORT TEXTS Sentiment extraction from Consumer-generated noisy short texts Hardik Meisheri TCS Innovation Labs New Delhi, India hardik.meisheri@tcs.comKunal Ranjan
JUMPING NLP CURVES: A REVIEW OF NATURAL LANGUAGE 50 IEEE COMPUTAT IONAL NT ELL G NCE MAGAZ | MAY 2014 Each rule is independent from the oth-ers, allowing rules to be added and deleted easily. Production rule systems have a DOWNLOADS « SENTICNET The latest version of the knowledge base can also be downloaded in OWL format as an ontology and selectively accessed online through an API, available in 40 different languages. Each language-dependent version of SenticNet is also downloadable in RDF/XML format as BabelSenticNet. SenticNet is also available in the form of embeddings as PROJECTS « SENTICNET At SenticNet, we are working on several projects spanning from fundamental affective computing research to the application of sentiment analysis techniques to domains like finance, healthcare, and the arts. Each project leverages the specific expertise of one or more members of the Sentic Team but, in fact, all projects are highly MULTI-ATTENTION RECURRENT NETWORK FOR HUMAN COMMUNICATION Multi-Attention Block Figure 1: Overview of Multi-attention Recurrent Network (MARN) with Long-short Term Hybrid Memory (LSTHM) and Multi-attention Block (MAB) components, for M = {l,v,a}representing the language, vision and acousticmodalities respectively. SENTIC PATTERNS: DEPENDENCY-BASED RULES FOR CONCEPT-LEVEL Sentic patterns: Dependency-based rules for concept-level sentiment analysis Soujanya Poriaa, Erik Cambriab,⇑, Grégoire Wintersteinc, Guang-Bin Huanga a School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore bSchool of Computer Engineering, Nanyang Technological University, Singapore cDepartment of Linguistics, University of Paris Diderot, France RECENT TRENDS IN DEEP LEARNING BASED PERSONALITY DETECTION 2318 Y.Mehtaetal. from text, non-verbal communication (e.g., interpersonal distances, speech and body move-ments), social media, mobiles, wearable devices and finally from computer games as well. MEMORY FUSION NETWORK FOR MULTI-VIEW SEQUENTIAL LEARNING ˇˆ˙˝˛ Delta-memory Attention Network Multi-view Gated Memory System of LSTMs ˚ ˚ ˜ ˆ˙˝ ˆ ˆ ˝ ˆ ! ˆ " ˆ # Figure 1: Overview figure of Memory Fusion Network (MFN) pipeline. σdenotes the sigmoidactivation function, τthe tanh activation function, ⊙the Hadamard product and ⊕element wise addition. MULTIMODAL SENTIMENT ANALYSIS USING HIERARCHICAL FUSION apply a deep Convolutional Neural Networks (CNN) on each ut-terance to extract textual features. Each utterance in the text isre-presented as an
ENSEMBLE APPLICATION OF CONVOLUTIONAL AND RECURRENT NEURALCONVOLUTIONAL VS RECURRENT NEURAL NETWORKSDIFFUSION CONVOLUTIONAL RECURRENT NEURAL NET…HYPERGRAPH CONVOLUTIONAL RECURRENT NEURAL …CONVOLUTIONAL NEURAL NETWORK PAPER3D CONVOLUTIONAL NEURAL NETWORKCONVOLUTIONAL NEURAL NETWORK TENSORFLOW Ensemble Application of Convolutional and Recurrent Neural Networks for Multi-label Text Categorization Guibin Chen 1, Deheng Ye , Zhenchang Xing2, Jieshan Chen3, Erik Cambria 1 School of Computer Science and Engineering, Nanyang Technological University, Singapore 2 Research School of Computer Science, Australian National University, Australia 3 School of Mathematics, Sun Yat-sen University UNDERSTANDING FILTER BUBBLES AND POLARIZATION IN SOCIAL Understanding Filter Bubbles and Polarization in Social Networks WISDOM’19, August 4, 2019, Anchorage, Alaska Other opinion dynamics models and metrics have also been used PHONETIC-BASED MICROTEXT NORMALIZATION FOR TWITTER Phonetic-Based Microtext Normalization for Twitter Sentiment Analysis Ranjan Satapathy, Claudia Guerreiro, Iti Chaturvedi, Erik Cambria Nanyang Technological University DOWNLOADS « SENTICNET The latest version of the knowledge base can also be downloaded in OWL format as an ontology and selectively accessed online through an API, available in 40 different languages. Each language-dependent version of SenticNet is also downloadable in RDF/XML format as BabelSenticNet. SenticNet is also available in the form of embeddings as PROJECTS « SENTICNET At SenticNet, we are working on several projects spanning from fundamental affective computing research to the application of sentiment analysis techniques to domains like finance, healthcare, and the arts. Each project leverages the specific expertise of one or more members of the Sentic Team but, in fact, all projects are highly MULTI-ATTENTION RECURRENT NETWORK FOR HUMAN COMMUNICATION Multi-Attention Block Figure 1: Overview of Multi-attention Recurrent Network (MARN) with Long-short Term Hybrid Memory (LSTHM) and Multi-attention Block (MAB) components, for M = {l,v,a}representing the language, vision and acousticmodalities respectively. SENTIC PATTERNS: DEPENDENCY-BASED RULES FOR CONCEPT-LEVEL Sentic patterns: Dependency-based rules for concept-level sentiment analysis Soujanya Poriaa, Erik Cambriab,⇑, Grégoire Wintersteinc, Guang-Bin Huanga a School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore bSchool of Computer Engineering, Nanyang Technological University, Singapore cDepartment of Linguistics, University of Paris Diderot, France RECENT TRENDS IN DEEP LEARNING BASED PERSONALITY DETECTION 2318 Y.Mehtaetal. from text, non-verbal communication (e.g., interpersonal distances, speech and body move-ments), social media, mobiles, wearable devices and finally from computer games as well. MEMORY FUSION NETWORK FOR MULTI-VIEW SEQUENTIAL LEARNING ˇˆ˙˝˛ Delta-memory Attention Network Multi-view Gated Memory System of LSTMs ˚ ˚ ˜ ˆ˙˝ ˆ ˆ ˝ ˆ ! ˆ " ˆ # Figure 1: Overview figure of Memory Fusion Network (MFN) pipeline. σdenotes the sigmoidactivation function, τthe tanh activation function, ⊙the Hadamard product and ⊕element wise addition. MULTIMODAL SENTIMENT ANALYSIS USING HIERARCHICAL FUSION apply a deep Convolutional Neural Networks (CNN) on each ut-terance to extract textual features. Each utterance in the text isre-presented as an
ENSEMBLE APPLICATION OF CONVOLUTIONAL AND RECURRENT NEURALCONVOLUTIONAL VS RECURRENT NEURAL NETWORKSDIFFUSION CONVOLUTIONAL RECURRENT NEURAL NET…HYPERGRAPH CONVOLUTIONAL RECURRENT NEURAL …CONVOLUTIONAL NEURAL NETWORK PAPER3D CONVOLUTIONAL NEURAL NETWORKCONVOLUTIONAL NEURAL NETWORK TENSORFLOW Ensemble Application of Convolutional and Recurrent Neural Networks for Multi-label Text Categorization Guibin Chen 1, Deheng Ye , Zhenchang Xing2, Jieshan Chen3, Erik Cambria 1 School of Computer Science and Engineering, Nanyang Technological University, Singapore 2 Research School of Computer Science, Australian National University, Australia 3 School of Mathematics, Sun Yat-sen University UNDERSTANDING FILTER BUBBLES AND POLARIZATION IN SOCIAL Understanding Filter Bubbles and Polarization in Social Networks WISDOM’19, August 4, 2019, Anchorage, Alaska Other opinion dynamics models and metrics have also been used PHONETIC-BASED MICROTEXT NORMALIZATION FOR TWITTER Phonetic-Based Microtext Normalization for Twitter Sentiment Analysis Ranjan Satapathy, Claudia Guerreiro, Iti Chaturvedi, Erik Cambria Nanyang Technological UniversityAPI - SENTICNET
Sentic API. Sentic API provides an easy and intuitive way to access SenticNet 6, a semantic network of commonsense knowledge that contains 200,000 nodes (words and multiword expressions) and thousands of connections (relationships between nodes), which is also available in different formats on our downloads page. Please note that Sentic API does not perform sentence-level sentiment analysis MEMORY FUSION NETWORK FOR MULTI-VIEW SEQUENTIAL LEARNING ˇˆ˙˝˛ Delta-memory Attention Network Multi-view Gated Memory System of LSTMs ˚ ˚ ˜ ˆ˙˝ ˆ ˆ ˝ ˆ ! ˆ " ˆ # Figure 1: Overview figure of Memory Fusion Network (MFN) pipeline. σdenotes the sigmoidactivation function, τthe tanh activation function, ⊙the Hadamard product and ⊕element wise addition. LEARNING COMMUNITY EMBEDDINGWITH COMMUNITY DETECTION AND Community Detection Community Embedding Node Embedding J/ J. J0 Figure 2: A closed loop for learning community embedding. Because each community is a group of densely connected nodes, RADICAL-BASED HIERARCHICAL EMBEDDINGS FOR CHINESE Radical-Based Hierarchical Embeddings for Chinese Sentiment Analysis at Sentence Level Haiyun Peng, Erik Cambria School of Computer Scienceand Engineering
WEAKLY SUPERVISED SEMANTIC SEGMENTATION WITH … WEAKLY SUPERVISED SEMANTIC SEGMENTATION WITH SUPERPIXEL EMBEDDING Frank Z. Xing 1, Erik Cambria , Win-Bin Huang2 and Yang Xu2 1 School of Computer Science and Engineering, Nanyang Technological University, Singapore 2 Department of Information Management, Peking University, China ABSTRACT In this paper, we propose to use contexts ofsuperpixels as a
ONE BELT, ONE ROAD, ONE SENTIMENT? A HYBRID APPROACH TO A literature review on this issue has highlighted Environ-ment, Energy, Project Operations, Economics and Trade as key themes in academic literature . A DEEPER LOOK INTO SARCASTIC TWEETS USING DEEP Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1601–1612, Osaka, Japan, December 11-17 2016.SENTIC
Sentic
SENTIMENT EXTRACTION FROM CONSUMER-GENERATED NOISY SHORT TEXTS Sentiment extraction from Consumer-generated noisy short texts Hardik Meisheri TCS Innovation Labs New Delhi, India hardik.meisheri@tcs.comKunal Ranjan
JUMPING NLP CURVES: A REVIEW OF NATURAL LANGUAGE 50 IEEE COMPUTAT IONAL NT ELL G NCE MAGAZ | MAY 2014 Each rule is independent from the oth-ers, allowing rules to be added and deleted easily. Production rule systems have aSENTICNET
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Talking about SenticNet is talking about concept-level sentiment analysis, that is, performing tasks such as polarity detection and emotion recognition by leveraging on semantics and linguistics in stead of solely relying on word co-occurrencefrequencies.
In this context, SenticNet can be one of the following things: 1) a concept-level knowledge base; 2) a multi-disciplinary framework; 3) a private company. As a _KNOWLEDGE BASE _, SenticNet provides a set of semantics, sentics, and polarity associated with 100,000 natural language concepts. In particular, semantics are concepts that are most semantically-related to the input concept (i.e., the five concepts that share more semantic features with the input concept), sentics are emotion categorization values expressed in terms of four affective dimensions (Pleasantness, Attention, Sensitivity, and Aptitude) and polarity is floating number between -1 and +1 (where -1 is extreme negativity and +1 is extreme positivity). The knowledge base is downloadable for free as a standalone XML file and its latest version (released every two years) is also accessible as an API. As a _FRAMEWORK _, SenticNet consists of a set of tools and techniques for sentiment analysis combining commonsense reasoning, psychology, linguistics, and machine learning. In this context, SenticNet is more commonly referred to as _sentic computing_, a multi-disciplinary paradigm that goes beyond mere statistical approaches to sentiment analysis by focusing on a semantic-preserving representation of natural language concepts and on sentence structure. As a _COMPANY _, finally, SenticNet puts together the latest findings in concept-level sentiment analysis to offer easy-to-use state-of-the-art tools for big social data analysis that enable the automation of tasks such as brand positioning, trend discovery, and social media marketing in different domains, languages,and modalities.
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