We show how effectively string kernels suit spam filtering problem . On the other hand , data preprocessing is a vital part of text classification where the objective is to generate feature vectors usable by SVM kernels . We detail a feature mapping variant in TC that yields improved performance for the standard SVM in filtering task . Our results are competitive with the most established text classification methods and our approach presents certain advantages , such as a good trade off between accuracy and scalability . Facing the particular challenges of the text classification problem , we also propose a new text preprocessing method that simultaneously performs both feature and instance selection . This intuitive method is computationally efficient and , most importantly , our experimental results show that it produces outstanding results . This paper covers a text classification problem : the identification of the author of a text . It is necessary to find author of a text with given information from a set of candidates whose sample texts were provided . Existing Machine Learning techniques yield close to human performance on text-based classification tasks . However , the presence of multi-modal noise in chat data such as emoticons , slang , spelling mistakes , code-mixed data , etc . makes existing deep-learning solutions perform poorly . Many sentiment analysis techniques have been proposed by researchers that classify and analyze the sentiments from micropost posted by various users . Majorly , these techniques perform text based classification that does not allow predicting the micropost impact . Further , it is very difficult to analyze this huge volume of online content produced each day . Online content filtering along with its own benefits has a big flaw ; filtering process affects data transfer throughput . Also this method suffers from being poor to analyze HTTP/HTTPS traffic like html reconstruction , HTML encoding , object analysis , text classifying , image analysis , analyzing HTTPS . Online content filtering is not effective if network traffic is encrypted and can not be decrypted by the online content filter or more generally , if the traffic is in a form that the online content filter can not interpret correctly . The paper considers two linguistic models , analyzed the possibility of their use for the text data classification as well as their associations in the integrated texts presentation . A cognitive approach for the text classification issues is presented . The increasing availability of text data motivates research for models able to improve accuracy in different language tasks . Following the image classification Convolutional Neural Network ( CNN ) tendency , the research in text classification has placed effort into developing deeper networks . The first CNN based approach for text was a shallow network with one layer . document : Practical Text Classification With Large Pre - Trained Language Models Multi - emotion sentiment classification is a natural language processing ( NLP ) problem with valuable use cases on real - world data . Estimating heart rate variability ( HRV ) and pulse rate variability ( PRV ) , 4 Poincare indices in 10 lags were extracted . The support vector machine classifier was used for emotion classification . Both feature level ( FL ) and decision level ( DL ) fusion schemes were examined . Standard classifiers based on Gaussian Mixture Models , Hidden Markov Models and Multilayer Perceptron are tested . These classifiers have been evaluated with different configurations and input features , in order to design a new hierarchical method for emotion classification . The proposed multiple feature hierarchical method for seven emotions , based on spectral and prosodic information , improves the performance over the standard classifiers and the fixed features . It also considers the scope of bio-potential signals , such as skin conductance and pulse count along with electroencephalographic signal on emotion detection of personals . Several algorithms of pattern classification have been examined to determine the best algorithm for the emotion classification problem . There are two way for combining genetic algorithm and neural networks , such as supportive approach and collaborative approach . This research proposed the supportive approach to developing an emotion-based classification system . The proposed system received frontal face image of human as input pattern and detected face and its facial feature regions , such as , mouth ( or lip ) , nose , and eyes . By the analysis of human face , it is seen that most of the emotional changes of the face occurs on eyes and lip . Therefore , two facial feature regions ( such as lip and eyes ) have been used for emotion-based classification . The GA has been used to optimize the facial features and finally the neural network has been used to classify facial features . This paper proposes a more general framework , latent affective mapping , which exploits two separate levels of semantic information : the first one encapsulates the foundations of the domain considered , while the second one specifically accounts for the overall affective fabric of the language . Exposing the emergent relationship between these two levels advantageously steers the emotion classification process . Empirical evidence suggests that this approach is effective for automatic emotion analysis in text . Neural networks are a family of statistical learning models inspired by biological neural networks and are used to estimate functions that can depend on a large number of inputs that are generally unknown . In this paper we seek to use this effectiveness of Neural Networks to classify user emotions using EEG signals from the DEAP ( Koelstra et al ( 2012 ) ) dataset which represents the benchmark for Emotion classification research . We explore 2 different Neural Models , a simple Deep Neural Network and a Convolutional Neural Network for classification . This study presents the deployment of deep learning network ( DLN ) to determine unspecified feature interrelationship between input data . The DLN is implemented with two semi-supervised algorithms i.e. ; Stack auto-encoder ( SAE ) and Deep Belief Network ( DBN ) individually on DEAP database for emotion state classification . A Bayesian inference classification based decision fusion method is used to improve the classification and the results has been improvised by almost 3 % in case of valence and arousal of DBN and almost 15 % and 6 % in SAE than the individual classification results of SAE and DBN , however in case of dominance the result has been improved by almost 9 % than SAE . Moreover , we present an improvement to proposed models by introducing latent features extracted from our internal Bio Auto-Encoder ( BAE ) . Both models are trained and evaluated on AMIGOS datasets reporting valence , arousal , and emotion state classification . Finally , to demonstrate a possible medical application in affective computing using deep learning techniques , we applied the proposed method to the assessment of anxiety therapy . This paper describes MuTuX , our system that is designed for task 1 - 5a , emotion classification analysis of tweets on SemEval20181 . The system aims at exploring the potential of context information of terms for emotion analysis . We show that a fully connected network of 26 neurons achieve an accuracy of 100 % on emotion detection , using only 2 % of memory available . Field trials demonstrate that the wearable device can achieve a 2-month lifetime while performing one emotion detection classification every 10 minutes . Our proposed FisherNet combines CNN training and FV encoding in a single end-to-end structure . We observe a clear advantage of FisherNet over plain CNN and standard FV in terms of both classification accuracy and computational efficiency on the challenging PASCAL visual object classes object classification and emotion image classification tasks . Overall , the metrics still beg the question of analyzing the crucial summarization questions of establishing semantic equivalence in the ways that content is expressed . Based on other summarization experiments performed in a demonstration project , we suggest that the solution to establishing equivalence and more specifically , when a concept represents an instantiation of a concept in a topic description , may be found in a richer ontological representation of documents . In this paper , we show that the performance measures Pk [ 1 ] and Window Diff [ 2 ] , commonly used for discourse , topic , and story segmentation evaluation , are biased in favor of segmentations with fewer or adjacent segment boundaries . By analytical and empirical means , we show how this results in a failure to penalize substantially defective segmentations . This guide will help you in topic formulation , how to write a thesis statement , and refer you to print and online resources for beginning your research and gathering quick facts and statistics . The Particular Case of Undergraduates Seeking Information for Course Assignments in the Humanities and Social Sciences To avoid information overload , undergraduates seeking information for course assignments in the humanities and social sciences might skip the necessary stages of topic definition and elaboration , as Kuhlthau describes in her six-stage Information Search Process ( ISP ) . This tendency can be reinforced by information professionals who seek to facilitate the users ' searching of electronic databases with the suggestion that they limit their search so that the end result will be " manageable . " Figure 3 : Topic Map Constraints Tree Structure Abstract Representation to Prolog Each maplet * structure is translated to Prolog clauses following the rules : Rule 1 : Closed maplet pattern The Prolog translation syntax for topic definition : topic(topic-id , X ) , X = [ ( value , type , scope ) , ... ] . The Prolog translation syntax for association definition : assoc(assoc-type , X ) , X = [ ( role , player ) , ... ] . To assess the quality of our corpus , we measure the inter-annotator agreements and classification accuracies on our UIT-VSFC . As a result , we achieved 91.20 % of the inter-annotator agreement for the sentiment-based task and 71.07 % of that for the topic-based task . In addition , the best results are of baseline model as the Maximum Entropy classifier with 87.94 % and 84.03 % of the overall F1-score of the sentiment-based and topic-based tasks respectively . However , previous studies evaluating the language modelling approach for information retrieval used different query sets and heterogeneous collections , which make reported results difficult to compare . This research is a broad-based study that evaluates language models against a variety of search tasks --- topic finding , named-page finding and topic distillation . The standard Text REtrieval Conference ( TREC ) methodology is used to compare language models to the probabilistic Okapi BM25 system . However , too few dimensions of presenting tweets hinder users from finding their interested information conveniently . In this demo , we presents “ Twitter+ ” , which aims to enrich user 's reading experiences in Twitter by providing multiple ways for them to explore tweets , such as keyword presentation , topic finding . It presents users an alternative interface to browse tweets more effectively . In this paper we present a new approach for topic spotting based on subword units and feature vectors instead of words . In our first approach , we only use vector quantized feature vectors and polygram language models for topic representation . In the second approach , we use phonemes instead of the vector quantized feature vectors and model the topics again using polygram language models . More importantly , INS can serve as a tool for editors to help them finish their jobs . In our experiments , INS performs well on both topic representation and synthesis article generation . A user study also demonstrates the usefulness and users ' satisfaction with the INS tool . Such knowledge also connects with language – namely , verbs for related actions . It can be used with an embedded reasoner , resulting in an effective system for QA , topic-listing , summarization and other tasks . This paper deals with the design of PurposeNet and its implementation . The feasibility of the proposed approach is examined by generating topics from more than one million tweets collected from Twitter during various events . The utility of these topics is demonstrated with a variety of topic-related tasks along with a comparison of the effort required to perform the same tasks with words-list-based representations . Manual evaluation of randomly selected 36 sets of topics yielded 81.0 % and 93.3 % for the precision and F1 scores respectively . BHSG includes two components : semantic generation module to learn semantic relevance between texts and topic enhance module to produce the text topic embedding based on text embedding learned in the former module . We evaluated our method on two kinds of topic-related tasks : text classification and information retrieval . The experimental results on four public datasets and one dataset we provide all demonstrate that our proposed method can achieve a better performance . As a new course , it differs from the traditional courses in formation , implementation , evaluation , etc . This paper discusses the course of reseach learning method in the aspects of topic - taking , teachers ' guidance , management of students as well as evaluation of the courses so as to understand and implement it better .
We show how effectively string kernels suit spam filtering problem . On the other hand , data preprocessing is a vital part of text classification where the objective is to generate feature vectors usable by SVM kernels . We detail a feature mapping variant in TC that yields improved performance for the standard SVM in filtering task .
Our results are competitive with the most established text classification methods and our approach presents certain advantages , such as a good trade off between accuracy and scalability . Facing the particular challenges of the text classification problem , we also propose a new text preprocessing method that simultaneously performs both feature and instance selection . This intuitive method is computationally efficient and , most importantly , our experimental results show that it produces outstanding results .
This paper covers a text classification problem : the identification of the author of a text . It is necessary to find author of a text with given information from a set of candidates whose sample texts were provided .
Existing Machine Learning techniques yield close to human performance on text-based classification tasks . However , the presence of multi-modal noise in chat data such as emoticons , slang , spelling mistakes , code-mixed data , etc . makes existing deep-learning solutions perform poorly .
Many sentiment analysis techniques have been proposed by researchers that classify and analyze the sentiments from micropost posted by various users . Majorly , these techniques perform text based classification that does not allow predicting the micropost impact . Further , it is very difficult to analyze this huge volume of online content produced each day .
Online content filtering along with its own benefits has a big flaw ; filtering process affects data transfer throughput . Also this method suffers from being poor to analyze HTTP/HTTPS traffic like html reconstruction , HTML encoding , object analysis , text classifying , image analysis , analyzing HTTPS . Online content filtering is not effective if network traffic is encrypted and can not be decrypted by the online content filter or more generally , if the traffic is in a form that the online content filter can not interpret correctly .
The paper considers two linguistic models , analyzed the possibility of their use for the text data classification as well as their associations in the integrated texts presentation . A cognitive approach for the text classification issues is presented .
The increasing availability of text data motivates research for models able to improve accuracy in different language tasks . Following the image classification Convolutional Neural Network ( CNN ) tendency , the research in text classification has placed effort into developing deeper networks . The first CNN based approach for text was a shallow network with one layer .
document : Practical Text Classification With Large Pre - Trained Language Models Multi - emotion sentiment classification is a natural language processing ( NLP ) problem with valuable use cases on real - world data .
Estimating heart rate variability ( HRV ) and pulse rate variability ( PRV ) , 4 Poincare indices in 10 lags were extracted . The support vector machine classifier was used for emotion classification . Both feature level ( FL ) and decision level ( DL ) fusion schemes were examined .
Standard classifiers based on Gaussian Mixture Models , Hidden Markov Models and Multilayer Perceptron are tested . These classifiers have been evaluated with different configurations and input features , in order to design a new hierarchical method for emotion classification . The proposed multiple feature hierarchical method for seven emotions , based on spectral and prosodic information , improves the performance over the standard classifiers and the fixed features .
It also considers the scope of bio-potential signals , such as skin conductance and pulse count along with electroencephalographic signal on emotion detection of personals . Several algorithms of pattern classification have been examined to determine the best algorithm for the emotion classification problem .
There are two way for combining genetic algorithm and neural networks , such as supportive approach and collaborative approach . This research proposed the supportive approach to developing an emotion-based classification system . The proposed system received frontal face image of human as input pattern and detected face and its facial feature regions , such as , mouth ( or lip ) , nose , and eyes . By the analysis of human face , it is seen that most of the emotional changes of the face occurs on eyes and lip . Therefore , two facial feature regions ( such as lip and eyes ) have been used for emotion-based classification . The GA has been used to optimize the facial features and finally the neural network has been used to classify facial features .
This paper proposes a more general framework , latent affective mapping , which exploits two separate levels of semantic information : the first one encapsulates the foundations of the domain considered , while the second one specifically accounts for the overall affective fabric of the language . Exposing the emergent relationship between these two levels advantageously steers the emotion classification process . Empirical evidence suggests that this approach is effective for automatic emotion analysis in text .
Neural networks are a family of statistical learning models inspired by biological neural networks and are used to estimate functions that can depend on a large number of inputs that are generally unknown . In this paper we seek to use this effectiveness of Neural Networks to classify user emotions using EEG signals from the DEAP ( Koelstra et al ( 2012 ) ) dataset which represents the benchmark for Emotion classification research . We explore 2 different Neural Models , a simple Deep Neural Network and a Convolutional Neural Network for classification .
This study presents the deployment of deep learning network ( DLN ) to determine unspecified feature interrelationship between input data . The DLN is implemented with two semi-supervised algorithms i.e. ; Stack auto-encoder ( SAE ) and Deep Belief Network ( DBN ) individually on DEAP database for emotion state classification . A Bayesian inference classification based decision fusion method is used to improve the classification and the results has been improvised by almost 3 % in case of valence and arousal of DBN and almost 15 % and 6 % in SAE than the individual classification results of SAE and DBN , however in case of dominance the result has been improved by almost 9 % than SAE .
Moreover , we present an improvement to proposed models by introducing latent features extracted from our internal Bio Auto-Encoder ( BAE ) . Both models are trained and evaluated on AMIGOS datasets reporting valence , arousal , and emotion state classification . Finally , to demonstrate a possible medical application in affective computing using deep learning techniques , we applied the proposed method to the assessment of anxiety therapy .
This paper describes MuTuX , our system that is designed for task 1 - 5a , emotion classification analysis of tweets on SemEval20181 . The system aims at exploring the potential of context information of terms for emotion analysis .
We show that a fully connected network of 26 neurons achieve an accuracy of 100 % on emotion detection , using only 2 % of memory available . Field trials demonstrate that the wearable device can achieve a 2-month lifetime while performing one emotion detection classification every 10 minutes .
Our proposed FisherNet combines CNN training and FV encoding in a single end-to-end structure . We observe a clear advantage of FisherNet over plain CNN and standard FV in terms of both classification accuracy and computational efficiency on the challenging PASCAL visual object classes object classification and emotion image classification tasks .
Overall , the metrics still beg the question of analyzing the crucial summarization questions of establishing semantic equivalence in the ways that content is expressed . Based on other summarization experiments performed in a demonstration project , we suggest that the solution to establishing equivalence and more specifically , when a concept represents an instantiation of a concept in a topic description , may be found in a richer ontological representation of documents .
In this paper , we show that the performance measures Pk [ 1 ] and Window Diff [ 2 ] , commonly used for discourse , topic , and story segmentation evaluation , are biased in favor of segmentations with fewer or adjacent segment boundaries . By analytical and empirical means , we show how this results in a failure to penalize substantially defective segmentations .
This guide will help you in topic formulation , how to write a thesis statement , and refer you to print and online resources for beginning your research and gathering quick facts and statistics .
The Particular Case of Undergraduates Seeking Information for Course Assignments in the Humanities and Social Sciences To avoid information overload , undergraduates seeking information for course assignments in the humanities and social sciences might skip the necessary stages of topic definition and elaboration , as Kuhlthau describes in her six-stage Information Search Process ( ISP ) . This tendency can be reinforced by information professionals who seek to facilitate the users ' searching of electronic databases with the suggestion that they limit their search so that the end result will be " manageable . "
Figure 3 : Topic Map Constraints Tree Structure Abstract Representation to Prolog Each maplet * structure is translated to Prolog clauses following the rules : Rule 1 : Closed maplet pattern The Prolog translation syntax for topic definition : topic(topic-id , X ) , X = [ ( value , type , scope ) , ... ] . The Prolog translation syntax for association definition : assoc(assoc-type , X ) , X = [ ( role , player ) , ... ] .
To assess the quality of our corpus , we measure the inter-annotator agreements and classification accuracies on our UIT-VSFC . As a result , we achieved 91.20 % of the inter-annotator agreement for the sentiment-based task and 71.07 % of that for the topic-based task . In addition , the best results are of baseline model as the Maximum Entropy classifier with 87.94 % and 84.03 % of the overall F1-score of the sentiment-based and topic-based tasks respectively .
However , previous studies evaluating the language modelling approach for information retrieval used different query sets and heterogeneous collections , which make reported results difficult to compare . This research is a broad-based study that evaluates language models against a variety of search tasks --- topic finding , named-page finding and topic distillation . The standard Text REtrieval Conference ( TREC ) methodology is used to compare language models to the probabilistic Okapi BM25 system .
However , too few dimensions of presenting tweets hinder users from finding their interested information conveniently . In this demo , we presents “ Twitter+ ” , which aims to enrich user 's reading experiences in Twitter by providing multiple ways for them to explore tweets , such as keyword presentation , topic finding . It presents users an alternative interface to browse tweets more effectively .
In this paper we present a new approach for topic spotting based on subword units and feature vectors instead of words . In our first approach , we only use vector quantized feature vectors and polygram language models for topic representation . In the second approach , we use phonemes instead of the vector quantized feature vectors and model the topics again using polygram language models .
More importantly , INS can serve as a tool for editors to help them finish their jobs . In our experiments , INS performs well on both topic representation and synthesis article generation . A user study also demonstrates the usefulness and users ' satisfaction with the INS tool .
Such knowledge also connects with language – namely , verbs for related actions . It can be used with an embedded reasoner , resulting in an effective system for QA , topic-listing , summarization and other tasks . This paper deals with the design of PurposeNet and its implementation .
The feasibility of the proposed approach is examined by generating topics from more than one million tweets collected from Twitter during various events . The utility of these topics is demonstrated with a variety of topic-related tasks along with a comparison of the effort required to perform the same tasks with words-list-based representations . Manual evaluation of randomly selected 36 sets of topics yielded 81.0 % and 93.3 % for the precision and F1 scores respectively .
BHSG includes two components : semantic generation module to learn semantic relevance between texts and topic enhance module to produce the text topic embedding based on text embedding learned in the former module . We evaluated our method on two kinds of topic-related tasks : text classification and information retrieval . The experimental results on four public datasets and one dataset we provide all demonstrate that our proposed method can achieve a better performance .
As a new course , it differs from the traditional courses in formation , implementation , evaluation , etc . This paper discusses the course of reseach learning method in the aspects of topic - taking , teachers ' guidance , management of students as well as evaluation of the courses so as to understand and implement it better .
On email spam filtering using support vector machine A unified view on association and classification mining and its applications Artificial Neural Network Techniques in Authorship Attribution NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep URWF: user reputation based weightage framework for twitter micropost classification Next generation filtering: Offline filtering enhanced proxy architecture for web content filtering Word embedding and cognitive linguistic models in text classification tasks Squeezed Very Deep Convolutional Neural Networks for Text Classification Practical Text Classification With Large Pre-Trained Language Models Fusion of heart rate variability and pulse rate variability for emotion recognition using lagged poincare plots Spoken emotion recognition using hierarchical classifiers Electroencephalographic Signal Processing for Detection and Prediction of Emotion Human Face Classification using Genetic Algorithm A Data-Driven Affective Analysis Framework Toward Naturally Expressive Speech Synthesis Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Dataset Multimodal affect recognition in V-A-D space using deep learning End-to-end facial and physiological model for \\Affective Computing and applications Mutux at SemEval-2018 Task 1: Exploring Impacts of Context Information On Emotion Detection DeepEmote: Towards multi-layer neural networks in a low power wearable multi-sensors bracelet Deep FisherNet for Image Classification Evolving XML Summarization Strategies in DUC 2005 Unbiased discourse segmentation evaluation Research Guides: Library Reference - Quick Help: Biographies The false focus in online searching : The particular case of undergraduates seeking information for course assignments in the humanities and social sciences PROLOG FOR TOPIC MAP CONSTRAINTS UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis A Comparative Study of Probabalistic and Language Models for Information Retrieval Twitter+: Build personalized newspaper for Twitter Topic Spotting Using Subword Units. INS: An Interactive Chinese News Synthesis System PurposeNet : An Ontological Resource Organized Around Purpose Microblog topic identification using Linked Open Data A Bidirectional Hierarchical Skip-Gram model for text topic embedding Brief Analysis On the Course of "Research Learning Method
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