A decision tree is an important means of data mining and inductive learning , which is usually used to form classifiers and prediction models . C4.5 is one of the most classic classification algorithms on data mining , but when it is used in mass calculations , the efficiency is very low . This book offers an overview of knowledge management . It starts with an introduction to the subject , placing descriptive models in the context of the overall field as well as within the more specific field of data mining analysis . Chapter 2 covers data visualization , including directions for accessing R open source software ( described through Rattle ) . The data were extracted from the information system of the healthcare network . We performed conventional statistical analysis and data mining analysis using mainly Bayesian networks . The Bayesian model showed that the probability of registration on the waiting list is associated to age , cardiovascular disease , diabetes , serum albumin level , respiratory disease , physical impairment , follow-up in the department performing transplantation and past history of malignancy . Data mining technology is gradually integrated into the decision support system , so to solveproblem of knowledge acquisition " bottleneck " facing by the traditional decision support system and other problems in series , and becomes an important part of the decision-making system . Data Mining Data mining also known as dada exploit and data excavation is an advanced process of extracting model ( knowledge ) that is potential , effective and easy for understanding according to set business object by using methods based on computer ( including new technologies ) . Prediction and description are two basic goals of the data mining . The paper concludes with the evaluation from the students and the reflection from the lecturers . Data Mining Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to intelligently exploit these large volumes of data . According to Chakrabarti et al. ( 2006 , p. 2 ) the central endeavor in data mining is to " extract knowledge from data " with this knowledge being captured " in a human-understandable structure . Each approach is suitable in different application scenarios . We stress that data analysis/mining of large datasets can be a supercomputer application . The All-k-Nearest-Neighbor ( AkNN ) operation is common in many applications such as GIS and data analysis/mining . In this paper , for the first time , we study a novel variant of AkNN queries , namely All-Visible-k-Nearest-Neighbor ( AVkNN ) query , which takes into account the impact of obstacles on the visibility of objects . Practical examples demonstrate the effectiveness and generality of methodological approaches and tools of knowledge extraction based on computer technology . The key points of the vast areas in the analysis of data data mining , and particularly its applications in scientific , technical and humanitarian areas are described . Variable Assessment Introduction Correlation Coefficient Scatterplots Data Mining Smoothed Scatterplot General Association Test Summary References CHAID-Based Data Mining for Paired-Variable Assessment Introduction The Scatterplot The Smooth Scatterplot Primer on CHAID CHAID-Based Data Mining for a Smoother Scatterplot Summary References Appendix The Importance of Straight Data : Simplicity and Desirability for Good Model-Building Practice Introduction Straightness and Symmetry in Data Data Mining Is a High Concept The Correlation Coefficient Scatterplot of ( xx3 , yy3 ) Data Mining the Relationship of ( xx3 , yy3 ) The role of data mining in cyber security is use to examine the data from various dimensions and approach searching the previously unknown hidden patterns classifying a grouping of the data and summarizing the identified relationships . Data mining of data and machine learning of data are important so that cyber data sets used in machine learning and data mining are described for cyber security is presented . Automatic processing for agriculture produces requires accurate identification of crops to target plants for treatment according to their needs . Wavelet analysis , which features spatial/frequency localization , data compression , denoising , and data analysis/data mining , is a good candidate for identifying crops . If the energy of wavelet packet coefficients is the sole identifying characteristic , however , results may vary significantly depending on factors such as weather , plant density , growth stage , and sunlight . Nowadays , data mining techniques are massively used via organizations intended for converting huge amount of data into information . Due to the advancement in database technology data are present at distributed sites consequently for carrying data mining analyzing in cost effective way we need to integrate these distributed data at one site . The predicament of anxiety here is that privacy of individual is at risk and we can find out sensitive information upon integration so a secure data model is needed to accomplish this task . How to extract useful knowledge from vast amounts of data become imperative . Data mining data is a processing techniques that came into being developed to comply with this requirement . Data Mining(Data Mining ) is a process that excavating interesting knowledge from large data stored in the database , data warehouse or other repository . This is based on the analogy of declarative query languages like SQL and query optimization which have made relational databases so successful . To this end , our proposed demo is not yet another data mining system , but of a new paradigm in data mining - mining with constraints , as the important first step towards supporting ad-hoc mining in DBMS . In this demo , we will show a prototype exploratory mining system that implements constraint-based mining query optimization methods proposed in [ 5 ] . Python has become the preferred programming language for computer scientists . In the fields of machine learning and data mining its use has also been promoted by the appearance of Python - based environments such as SciPy and scikit - learn , whose potential and ease of use have attracted the interest of practitioners . Our reimplementation is thus in line with these trends . According to the number of desired categories , text classification can be divided into two groups , namely , multi - label ( multiple categories ) and multi - class ( unique category ) . For instance , classifying an article into different topics ( e.g. , machine learning or data mining ) falls into the former one since an article could be under several topics simultaneously . By contrast , classifying a comment of a movie into its corresponding rating level lies into the multi - class group . With the recently increasing interest for opinion mining from different research communities , there is an evolving body of work on Arabic Sentiment Analysis . There are few available polarity annotated datasets for this language , so most existing works use these datasets to test the best known supervised algorithms for their objectives . Public can come to a decision about the factors through these opinions and reviews . In such cases , if spam reviews are present , it will affect the entire opinion mining process . Spam reviews would be posted with the intention of falsely promoting a product . With the rise in use of micro-blogging sites like Twitter , people are able to express and share their opinions with each other on a common platform . Currently all work in opinion mining research has quantified & assessed the expression of opinion as positive , negative or neutral values , we intend to categorize the opinion on the basis of five emotions , namely Happiness , Anger , Fear , Sadness & Disgust , which have been globally accepted & defined in human psychology . This paper presents a method to assess these identified types of emotions in a tweet using opinion mining . The process of selecting the algorithms and their parameters is modelled as an optimization problem and a technique was proposed to find an optimal combination based on the metaheuristic Population-Based Incremental Learning ( PBIL ) . For validation purposes , this approach is applied to a standard opinion mining problem . NLP-Opt effectively optimizes the algorithms and parameters of pipelines . In our paper , we focus on using Twitter , the most popular microblogging platform , for the task of sentiment analysis . We show how to automatically collect a corpus for sentiment analysis and opinion mining purposes . We perform linguistic analysis of the collected corpus and explain discovered phenomena . Information available from social networks is beneficial for analysis of user opinion , for example measuring the feedback on a recently released product , movie or a MOOC , policy change or ongoing events . Using Social network APIs , Python NLTK , automatic collection of a corpus for sentiment analysis and opinion mining purposes is done . Using the corpus we build a sentiment classifier based on Naive Bayes classification that is very accurate , that is able to determine positive , negative and objective sentiments for a document with specificity factor . However , despite the country 's incessant efforts for the past five years , scales and solutions through which their performance levels can be verified are not being suggested . Therefore , this thesis had conducted an opinion mining analysis with South Korean online media as target by using semi-structured data for obtaining a quantitative scale and verification , while excluding the existing methods of analysis via checklists and questionnaires . The directivity of educational policies which South Korea is not obliged but has the option to pursue has been suggested through the analyzation of social impacts and public awareness on creative education currently being enforced via the opinion mining analysis . Ordinal regression ( also known as ordinal classification ) is a supervised learning task that consists of automatically determining the implied rating of a data item on a fixed , discrete rating scale . This problem is receiving increasing attention from the sentiment analysis and opinion mining community , due to the importance of automatically rating increasing amounts of product review data in digital form . As in other supervised learning tasks such as ( binary or multiclass ) classification , feature selection is needed in order to improve efficiency and to avoid overfitting . Ordinal classification ( also known as ordinal regression ) is a supervised learning task that consists of estimating the rating of a data item on a fixed , discrete rating scale . This problem is receiving increased attention from the sentiment analysis and opinion mining community due to the importance of automatically rating large amounts of product review data in digital form . As in other supervised learning tasks such as binary or multiclass classification , feature selection is often needed in order to improve efficiency and avoid overfitting . That ’s being as a good platform for tracking and analyzing public sentiment . Nowadays the one of the important data mining research is sentiment analysis mining or opinion mining analyzing . That ’s why the sentiment analysis is attracting the researchers to find the critical information for decision making purpose in both of academic sides and also industry sides . One of the main reasons identified , why the initiatives fall short on participation , is the problem of digital divide and social exclusion . We present a study on eParticipation in municipalities , based on an opinion-mining project in an Irish city . We provide preliminary results and our conclusions after conducting a specific initiative . They were able to achieve good results on POS tagging , chunking , NER and semantic role labeling , without relying on hand - engineered features . Irsoy2014a experimented with multi - layer bidirectional Elman - style recurrent networks , and found that the deep models outperformed conditional random fields on the task of opinion mining . Huang2015 described a bidirectional LSTM model with a CRF layer , which included hand - crafted features specialised for the task of named entity recognition . License details : Sentiment analysis , also known as opinion mining , is a fundamental task in natural language processing and computational linguistics . Sentiment analysis is crucial to understanding user generated text in social networks or product reviews , and has drawn a lot of attentions from both industry and academic communities . document : DialogueRNN : An Attentive RNN for Emotion Detection in Conversations Emotion detection in conversations is a necessary step for a number of applications , including opinion mining over chat history , social media threads , debates , argumentation mining , understanding consumer feedback in live conversations , etc . Currently systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance . Sequential pattern mining is an important data mining problem with broad application . The paper discusses the incremental update of frequent sequences , and a new algorithm Inc_SPM(Incremental update of Sequential Pattern Mining ) based on the algorithm of PrefixSpan is provided . The paper discusses the results of experiments with a new context extension of a sequential pattern mining problem . In this extension , two kinds of context attributes are introduced for describing the source of a sequence and for each element in- side this sequence . Constraints are essential for many sequential pattern mining applications . However , there is no systematic study on constraint-based sequential pattern mining . In this paper , I present a Constrained Prefixspan algorithm which generates sequential patterns by using prefix projected pattern growth approach . Except the frequency this algorithm also applies gap , compactness and recency constraints in sequential pattern mining process . The gap constraint imposes limit on the separation of two consecutive transactions of discovered patterns , recency constraint causes patterns to quickly adapt the latest behaviors and compactness constraint ensures reasonable time spans for the discovered patterns . Many studies in CRM show that not all customers have the same contribution to business , and , to maximize business profit , it is necessary to evaluate customer value before the design of effective marketing strategies . In this study , we include the concepts of RFM analysis into sequential pattern mining process . For a given subsequence , each customer sequence contributes its own recency , frequency , and monetary scores to represent customer importance . This paper will consider the problem of sequential mining patterns embedded in a database by handling the time constraints as defined in the GSP algorithm ( level wise algorithms ) . We will compare two previous approaches GTC and PSP , that resumes the general principles of GSP . Sequential pattern mining is an active field in the domain of knowledge discovery . Recently , with the constant progress in hardware technologies , real-world databases tend to grow larger and the hypothesis that a database can be loaded into main-memory for sequential pattern mining purpose is no longer valid . Furthermore , the new model of data as a continuous and potentially infinite flow , known as data stream model , call for a pre-processing step to ease the mining operations . Argument Mining ( AM ) is a relatively recent discipline , which concentrates on extracting claims or premises from discourses , and inferring their structures . However , many existing works do not consider micro-level AM studies on discussion threads sufficiently . We present algorithms for finding both consistent and contrastive expansions and demonstrate their effectiveness empirically . We suggest that debate topic expansion may have various use cases in argumentation mining . ABSTRACT We solve the argument mining problem by investigating discourse and communicative text structure . A new formal graph-based structure called communicative discourse tree ( CDT ) is defined . This is a subset of NLP that deals with extracting arguments and the relations among them from user-based content . A major problem faced by argument mining research is the lack of human-annotated data . In this paper , we investigate the use of weakly supervised and semi-supervised methods for automatically annotating data , and thus providing large annotated datasets . Argument mining processes in the legal domain it is aiming to detect and extract the premises , claims and their relations automatically from unstructured legal texts to provide structured data that can be processable by argumentation models . This paper presents a framework to detect and identify the components of arguments in texts of Arabic legal documents . Propositions are the basic units of an argument and the primary building blocks of most argument mining systems . However , they are usually substituted by argumentative discourse units obtained via surface-level text segmentation , which may yield text segments that lack semantic information necessary for subsequent argument mining processes . In contrast , our cascade model aims to extract complete propositions by handling anaphora resolution , text segmentation , reported speech , questions , imperatives , missing subjects , and revision . We show how these methods can help the argumentation mining process , but also demonstrate that these methods need further research to automatically acquire the necessary background knowledge and more specifically common sense and world knowledge . We propose a number of ways to improve the learning of common sense and world knowledge by exploiting textual and visual data , and touch upon how we can integrate the learned knowledge in the argumentation mining process . In this article , we focus on analyzing whether news headlines support tweets and whether reviews are deceptive by analyzing the interaction or the influence that these texts have on the others , thus exploiting contextual information . Concretely , we define a deep learning method for relation – based argument mining to extract argumentative relations of attack and support . We then use this method for determining whether news articles support tweets , a useful task in fact-checking settings , where determining agreement toward a statement is a useful step toward determining its truthfulness . Argument mining studies in natural language text often use lexical ( e.g. n-grams ) and syntactic ( e.g. grammatical production rules ) features with all possible values . In prior work on a corpus of academic essays , we demonstrated that such large and sparse feature spaces can cause difficulty for feature selection and proposed a method to design a more compact feature space . document : DialogueRNN : An Attentive RNN for Emotion Detection in Conversations Emotion detection in conversations is a necessary step for a number of applications , including opinion mining over chat history , social media threads , debates , argumentation mining , understanding consumer feedback in live conversations , etc . Currently systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance . Lectures translation is a case of spoken language translation and there is a lack of publicly available parallel corpora for this purpose . To address this , we examine a language independent framework for parallel corpus mining which is a quick and effective way to mine a parallel corpus from publicly available lectures at Coursera . Our approach determines sentence alignments , relying on machine translation and cosine similarity over continuous-space sentence representations . We introduce ( 1 ) a novel stochastic inversion transduction grammar formalism for bilingual language modeling of sentence-pairs , and ( 2 ) the concept of bilingual parsing with a variety of parallel corpus analysis applications . Aside from the bilingual orientation , three major features distinguish the formalism from the finite-state transducers more traditionally found in computational linguistics : it skips directly to a context-free rather than finite-state base , it permits a minimal extra degree of ordering flexibility , and its probabilistic formulation admits an efficient maximum-likelihood bilingual parsing algorithm . We introduce ( 1 ) a novel stochastic inversion transduction grammar formalism for bilingual language modeling of sentence-pairs , and ( 2 ) the concept of bilingual parsing with a variety of parallel corpus analysis applications . Aside from the bilingual orientation , three major features distinguish the formalism from the finite-state transducers more traditionally found in computational linguistics : it skips directly to a context-free rather than finite-state base , it permits a minimal extra degree of ordering flexibility , and its probabilistic formulation admits an efficient maximum-likelihood bilingual parsing algorithm . ’ The present study examines English-Korean translational strategies of ‘ be being + predicative adjective ’ constructions , as this is an area where a grammatical mismatch exists between the source and target languages . To this end , a parallel corpus analysis was conducted using the Harry Potter series , which consists of seven fantasy novels . Among the 36 translated sentences analyzed and classified , 22 ( 61.1 % ) were regarded as preserving the meaning of the source texts ( STs ) , whereas the remaining 14 ( 38.9 % ) were considered to be cases where interventions were made by the translator . We introduce ( 1 ) a novel stochastic inversion transduction grammar formalism for bilingual language modeling of sentence-pairs and ( 2 ) the concept of bilingual parsing with potential application to a variety of parallel corpus analysis problems The formalism combines three tactics against the constraints that render finite-state transducers less useful it skips directly to a context-free rather than finite-state base it permits a minimal extra degree of ordering flexibility and its probabilistic formulation admits an efficient maximum-likelihood bilingual parsing algorithm A convenient normal form is shown to exist and we discuss a number of examples ot how stochastic inversion transduction grammars bring bilingual constraints to bear upon problematic corpus analysis tasks . Parallel corpus-based classification and description of uncertainty markers in the Russian language The paper is aimed both to classify Russian markers of uncertainty and to set up a methodology necessary for their investigation . Also , it presents an original scheme devised for describing these linguistic units . Corpus may be considered as fuel for the data driven approaches of machine translation . Parallel corpus building is a labour intensive task , thus making it a costly and scarce resource . Full potential of available data needs to be exploited and this can be ensured by removing different types of inconsistencies as being faced throughout the NLP domain . The construction of the parallel corpus facilitates the identification and extraction of English health risk terms which gave rise to competing translations or the detected terminological variation in health translation , the translation phenomenon highlighted in this chapter and discussed at length in this book . The variant translations extracted in the parallel corpus exploration were grouped into different clusters of health risk terminologies that underscored the development and building of new health risks knowledge among the target audiences . Better Splitting Algorithms for Parallel Corpus Processing Each iteration of minimum error rate training involves re-translating a development set . Distributing this work across computational nodes can speed up translation time , but in practice some parts may take much longer to complete than others , leading to computational slack time . A review of typical parallel FPM algorithms uncovers the major challenges , methodologies , and research problems in the field of parallel frequent pattern discovery , such as work-load balancing , finding good data layouts , and data decomposition . This survey also indicates a dramatic shift of the research interest in the field from the simple parallel frequent itemset mining on traditional parallel and distributed platforms to parallel pattern mining of more complex data on emerging architectures , such as multi-core systems and the increasingly mature grid infrastructure . Second , we describe a combined anchor-point-based method for comparable sentences alignment . Third , we present a compositional-based approach for parallel phrase mining . We conducted multiple evaluations to qualify the extracted comparable and parallel data . We also achieve very competitive results in cross - lingual document classification ( MLDoc dataset ) . Our sentence embeddings are also strong at parallel corpus mining , establishing a new state - of - the - art in the BUCC shared task for 3 of its 4 language pairs . Finally , we introduce a new test set of aligned sentences in 122 languages based on the Tatoeba corpus , and show that our sentence embeddings obtain strong results in multilingual similarity search even for low - resource languages . Our experiments show large improvements over existing methods . We outperform the best published results on the BUCC shared task on parallel corpus mining by more than 10 F1 points . We also improve the precision from 48.9 to 83.3 on the reconstruction of 11.3 M English - French sentence pairs of the UN corpus .
A decision tree is an important means of data mining and inductive learning , which is usually used to form classifiers and prediction models . C4.5 is one of the most classic classification algorithms on data mining , but when it is used in mass calculations , the efficiency is very low .
This book offers an overview of knowledge management . It starts with an introduction to the subject , placing descriptive models in the context of the overall field as well as within the more specific field of data mining analysis . Chapter 2 covers data visualization , including directions for accessing R open source software ( described through Rattle ) .
The data were extracted from the information system of the healthcare network . We performed conventional statistical analysis and data mining analysis using mainly Bayesian networks . The Bayesian model showed that the probability of registration on the waiting list is associated to age , cardiovascular disease , diabetes , serum albumin level , respiratory disease , physical impairment , follow-up in the department performing transplantation and past history of malignancy .
Data mining technology is gradually integrated into the decision support system , so to solveproblem of knowledge acquisition " bottleneck " facing by the traditional decision support system and other problems in series , and becomes an important part of the decision-making system . Data Mining Data mining also known as dada exploit and data excavation is an advanced process of extracting model ( knowledge ) that is potential , effective and easy for understanding according to set business object by using methods based on computer ( including new technologies ) . Prediction and description are two basic goals of the data mining .
The paper concludes with the evaluation from the students and the reflection from the lecturers . Data Mining Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to intelligently exploit these large volumes of data . According to Chakrabarti et al. ( 2006 , p. 2 ) the central endeavor in data mining is to " extract knowledge from data " with this knowledge being captured " in a human-understandable structure .
Each approach is suitable in different application scenarios . We stress that data analysis/mining of large datasets can be a supercomputer application .
The All-k-Nearest-Neighbor ( AkNN ) operation is common in many applications such as GIS and data analysis/mining . In this paper , for the first time , we study a novel variant of AkNN queries , namely All-Visible-k-Nearest-Neighbor ( AVkNN ) query , which takes into account the impact of obstacles on the visibility of objects .
Practical examples demonstrate the effectiveness and generality of methodological approaches and tools of knowledge extraction based on computer technology . The key points of the vast areas in the analysis of data data mining , and particularly its applications in scientific , technical and humanitarian areas are described .
Variable Assessment Introduction Correlation Coefficient Scatterplots Data Mining Smoothed Scatterplot General Association Test Summary References CHAID-Based Data Mining for Paired-Variable Assessment Introduction The Scatterplot The Smooth Scatterplot Primer on CHAID CHAID-Based Data Mining for a Smoother Scatterplot Summary References Appendix The Importance of Straight Data : Simplicity and Desirability for Good Model-Building Practice Introduction Straightness and Symmetry in Data Data Mining Is a High Concept The Correlation Coefficient Scatterplot of ( xx3 , yy3 ) Data Mining the Relationship of ( xx3 , yy3 )
The role of data mining in cyber security is use to examine the data from various dimensions and approach searching the previously unknown hidden patterns classifying a grouping of the data and summarizing the identified relationships . Data mining of data and machine learning of data are important so that cyber data sets used in machine learning and data mining are described for cyber security is presented .
Automatic processing for agriculture produces requires accurate identification of crops to target plants for treatment according to their needs . Wavelet analysis , which features spatial/frequency localization , data compression , denoising , and data analysis/data mining , is a good candidate for identifying crops . If the energy of wavelet packet coefficients is the sole identifying characteristic , however , results may vary significantly depending on factors such as weather , plant density , growth stage , and sunlight .
Nowadays , data mining techniques are massively used via organizations intended for converting huge amount of data into information . Due to the advancement in database technology data are present at distributed sites consequently for carrying data mining analyzing in cost effective way we need to integrate these distributed data at one site . The predicament of anxiety here is that privacy of individual is at risk and we can find out sensitive information upon integration so a secure data model is needed to accomplish this task .
How to extract useful knowledge from vast amounts of data become imperative . Data mining data is a processing techniques that came into being developed to comply with this requirement . Data Mining(Data Mining ) is a process that excavating interesting knowledge from large data stored in the database , data warehouse or other repository .
This is based on the analogy of declarative query languages like SQL and query optimization which have made relational databases so successful . To this end , our proposed demo is not yet another data mining system , but of a new paradigm in data mining - mining with constraints , as the important first step towards supporting ad-hoc mining in DBMS . In this demo , we will show a prototype exploratory mining system that implements constraint-based mining query optimization methods proposed in [ 5 ] .
Python has become the preferred programming language for computer scientists . In the fields of machine learning and data mining its use has also been promoted by the appearance of Python - based environments such as SciPy and scikit - learn , whose potential and ease of use have attracted the interest of practitioners . Our reimplementation is thus in line with these trends .
According to the number of desired categories , text classification can be divided into two groups , namely , multi - label ( multiple categories ) and multi - class ( unique category ) . For instance , classifying an article into different topics ( e.g. , machine learning or data mining ) falls into the former one since an article could be under several topics simultaneously . By contrast , classifying a comment of a movie into its corresponding rating level lies into the multi - class group .
With the recently increasing interest for opinion mining from different research communities , there is an evolving body of work on Arabic Sentiment Analysis . There are few available polarity annotated datasets for this language , so most existing works use these datasets to test the best known supervised algorithms for their objectives .
Public can come to a decision about the factors through these opinions and reviews . In such cases , if spam reviews are present , it will affect the entire opinion mining process . Spam reviews would be posted with the intention of falsely promoting a product .
With the rise in use of micro-blogging sites like Twitter , people are able to express and share their opinions with each other on a common platform . Currently all work in opinion mining research has quantified & assessed the expression of opinion as positive , negative or neutral values , we intend to categorize the opinion on the basis of five emotions , namely Happiness , Anger , Fear , Sadness & Disgust , which have been globally accepted & defined in human psychology . This paper presents a method to assess these identified types of emotions in a tweet using opinion mining .
The process of selecting the algorithms and their parameters is modelled as an optimization problem and a technique was proposed to find an optimal combination based on the metaheuristic Population-Based Incremental Learning ( PBIL ) . For validation purposes , this approach is applied to a standard opinion mining problem . NLP-Opt effectively optimizes the algorithms and parameters of pipelines .
In our paper , we focus on using Twitter , the most popular microblogging platform , for the task of sentiment analysis . We show how to automatically collect a corpus for sentiment analysis and opinion mining purposes . We perform linguistic analysis of the collected corpus and explain discovered phenomena .
Information available from social networks is beneficial for analysis of user opinion , for example measuring the feedback on a recently released product , movie or a MOOC , policy change or ongoing events . Using Social network APIs , Python NLTK , automatic collection of a corpus for sentiment analysis and opinion mining purposes is done . Using the corpus we build a sentiment classifier based on Naive Bayes classification that is very accurate , that is able to determine positive , negative and objective sentiments for a document with specificity factor .
However , despite the country 's incessant efforts for the past five years , scales and solutions through which their performance levels can be verified are not being suggested . Therefore , this thesis had conducted an opinion mining analysis with South Korean online media as target by using semi-structured data for obtaining a quantitative scale and verification , while excluding the existing methods of analysis via checklists and questionnaires . The directivity of educational policies which South Korea is not obliged but has the option to pursue has been suggested through the analyzation of social impacts and public awareness on creative education currently being enforced via the opinion mining analysis .
Ordinal regression ( also known as ordinal classification ) is a supervised learning task that consists of automatically determining the implied rating of a data item on a fixed , discrete rating scale . This problem is receiving increasing attention from the sentiment analysis and opinion mining community , due to the importance of automatically rating increasing amounts of product review data in digital form . As in other supervised learning tasks such as ( binary or multiclass ) classification , feature selection is needed in order to improve efficiency and to avoid overfitting .
Ordinal classification ( also known as ordinal regression ) is a supervised learning task that consists of estimating the rating of a data item on a fixed , discrete rating scale . This problem is receiving increased attention from the sentiment analysis and opinion mining community due to the importance of automatically rating large amounts of product review data in digital form . As in other supervised learning tasks such as binary or multiclass classification , feature selection is often needed in order to improve efficiency and avoid overfitting .
That ’s being as a good platform for tracking and analyzing public sentiment . Nowadays the one of the important data mining research is sentiment analysis mining or opinion mining analyzing . That ’s why the sentiment analysis is attracting the researchers to find the critical information for decision making purpose in both of academic sides and also industry sides .
One of the main reasons identified , why the initiatives fall short on participation , is the problem of digital divide and social exclusion . We present a study on eParticipation in municipalities , based on an opinion-mining project in an Irish city . We provide preliminary results and our conclusions after conducting a specific initiative .
They were able to achieve good results on POS tagging , chunking , NER and semantic role labeling , without relying on hand - engineered features . Irsoy2014a experimented with multi - layer bidirectional Elman - style recurrent networks , and found that the deep models outperformed conditional random fields on the task of opinion mining . Huang2015 described a bidirectional LSTM model with a CRF layer , which included hand - crafted features specialised for the task of named entity recognition .
License details : Sentiment analysis , also known as opinion mining , is a fundamental task in natural language processing and computational linguistics . Sentiment analysis is crucial to understanding user generated text in social networks or product reviews , and has drawn a lot of attentions from both industry and academic communities .
document : DialogueRNN : An Attentive RNN for Emotion Detection in Conversations Emotion detection in conversations is a necessary step for a number of applications , including opinion mining over chat history , social media threads , debates , argumentation mining , understanding consumer feedback in live conversations , etc . Currently systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance .
Sequential pattern mining is an important data mining problem with broad application . The paper discusses the incremental update of frequent sequences , and a new algorithm Inc_SPM(Incremental update of Sequential Pattern Mining ) based on the algorithm of PrefixSpan is provided .
The paper discusses the results of experiments with a new context extension of a sequential pattern mining problem . In this extension , two kinds of context attributes are introduced for describing the source of a sequence and for each element in- side this sequence .
Constraints are essential for many sequential pattern mining applications . However , there is no systematic study on constraint-based sequential pattern mining .
In this paper , I present a Constrained Prefixspan algorithm which generates sequential patterns by using prefix projected pattern growth approach . Except the frequency this algorithm also applies gap , compactness and recency constraints in sequential pattern mining process . The gap constraint imposes limit on the separation of two consecutive transactions of discovered patterns , recency constraint causes patterns to quickly adapt the latest behaviors and compactness constraint ensures reasonable time spans for the discovered patterns .
Many studies in CRM show that not all customers have the same contribution to business , and , to maximize business profit , it is necessary to evaluate customer value before the design of effective marketing strategies . In this study , we include the concepts of RFM analysis into sequential pattern mining process . For a given subsequence , each customer sequence contributes its own recency , frequency , and monetary scores to represent customer importance .
This paper will consider the problem of sequential mining patterns embedded in a database by handling the time constraints as defined in the GSP algorithm ( level wise algorithms ) . We will compare two previous approaches GTC and PSP , that resumes the general principles of GSP .
Sequential pattern mining is an active field in the domain of knowledge discovery . Recently , with the constant progress in hardware technologies , real-world databases tend to grow larger and the hypothesis that a database can be loaded into main-memory for sequential pattern mining purpose is no longer valid . Furthermore , the new model of data as a continuous and potentially infinite flow , known as data stream model , call for a pre-processing step to ease the mining operations .
Argument Mining ( AM ) is a relatively recent discipline , which concentrates on extracting claims or premises from discourses , and inferring their structures . However , many existing works do not consider micro-level AM studies on discussion threads sufficiently .
We present algorithms for finding both consistent and contrastive expansions and demonstrate their effectiveness empirically . We suggest that debate topic expansion may have various use cases in argumentation mining .
ABSTRACT We solve the argument mining problem by investigating discourse and communicative text structure . A new formal graph-based structure called communicative discourse tree ( CDT ) is defined .
This is a subset of NLP that deals with extracting arguments and the relations among them from user-based content . A major problem faced by argument mining research is the lack of human-annotated data . In this paper , we investigate the use of weakly supervised and semi-supervised methods for automatically annotating data , and thus providing large annotated datasets .
Argument mining processes in the legal domain it is aiming to detect and extract the premises , claims and their relations automatically from unstructured legal texts to provide structured data that can be processable by argumentation models . This paper presents a framework to detect and identify the components of arguments in texts of Arabic legal documents .
Propositions are the basic units of an argument and the primary building blocks of most argument mining systems . However , they are usually substituted by argumentative discourse units obtained via surface-level text segmentation , which may yield text segments that lack semantic information necessary for subsequent argument mining processes . In contrast , our cascade model aims to extract complete propositions by handling anaphora resolution , text segmentation , reported speech , questions , imperatives , missing subjects , and revision .
We show how these methods can help the argumentation mining process , but also demonstrate that these methods need further research to automatically acquire the necessary background knowledge and more specifically common sense and world knowledge . We propose a number of ways to improve the learning of common sense and world knowledge by exploiting textual and visual data , and touch upon how we can integrate the learned knowledge in the argumentation mining process .
In this article , we focus on analyzing whether news headlines support tweets and whether reviews are deceptive by analyzing the interaction or the influence that these texts have on the others , thus exploiting contextual information . Concretely , we define a deep learning method for relation – based argument mining to extract argumentative relations of attack and support . We then use this method for determining whether news articles support tweets , a useful task in fact-checking settings , where determining agreement toward a statement is a useful step toward determining its truthfulness .
Argument mining studies in natural language text often use lexical ( e.g. n-grams ) and syntactic ( e.g. grammatical production rules ) features with all possible values . In prior work on a corpus of academic essays , we demonstrated that such large and sparse feature spaces can cause difficulty for feature selection and proposed a method to design a more compact feature space .
document : DialogueRNN : An Attentive RNN for Emotion Detection in Conversations Emotion detection in conversations is a necessary step for a number of applications , including opinion mining over chat history , social media threads , debates , argumentation mining , understanding consumer feedback in live conversations , etc . Currently systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance .
Lectures translation is a case of spoken language translation and there is a lack of publicly available parallel corpora for this purpose . To address this , we examine a language independent framework for parallel corpus mining which is a quick and effective way to mine a parallel corpus from publicly available lectures at Coursera . Our approach determines sentence alignments , relying on machine translation and cosine similarity over continuous-space sentence representations .
We introduce ( 1 ) a novel stochastic inversion transduction grammar formalism for bilingual language modeling of sentence-pairs , and ( 2 ) the concept of bilingual parsing with a variety of parallel corpus analysis applications . Aside from the bilingual orientation , three major features distinguish the formalism from the finite-state transducers more traditionally found in computational linguistics : it skips directly to a context-free rather than finite-state base , it permits a minimal extra degree of ordering flexibility , and its probabilistic formulation admits an efficient maximum-likelihood bilingual parsing algorithm .
We introduce ( 1 ) a novel stochastic inversion transduction grammar formalism for bilingual language modeling of sentence-pairs , and ( 2 ) the concept of bilingual parsing with a variety of parallel corpus analysis applications . Aside from the bilingual orientation , three major features distinguish the formalism from the finite-state transducers more traditionally found in computational linguistics : it skips directly to a context-free rather than finite-state base , it permits a minimal extra degree of ordering flexibility , and its probabilistic formulation admits an efficient maximum-likelihood bilingual parsing algorithm .
’ The present study examines English-Korean translational strategies of ‘ be being + predicative adjective ’ constructions , as this is an area where a grammatical mismatch exists between the source and target languages . To this end , a parallel corpus analysis was conducted using the Harry Potter series , which consists of seven fantasy novels . Among the 36 translated sentences analyzed and classified , 22 ( 61.1 % ) were regarded as preserving the meaning of the source texts ( STs ) , whereas the remaining 14 ( 38.9 % ) were considered to be cases where interventions were made by the translator .
We introduce ( 1 ) a novel stochastic inversion transduction grammar formalism for bilingual language modeling of sentence-pairs and ( 2 ) the concept of bilingual parsing with potential application to a variety of parallel corpus analysis problems The formalism combines three tactics against the constraints that render finite-state transducers less useful it skips directly to a context-free rather than finite-state base it permits a minimal extra degree of ordering flexibility and its probabilistic formulation admits an efficient maximum-likelihood bilingual parsing algorithm A convenient normal form is shown to exist and we discuss a number of examples ot how stochastic inversion transduction grammars bring bilingual constraints to bear upon problematic corpus analysis tasks .
Parallel corpus-based classification and description of uncertainty markers in the Russian language The paper is aimed both to classify Russian markers of uncertainty and to set up a methodology necessary for their investigation . Also , it presents an original scheme devised for describing these linguistic units .
Corpus may be considered as fuel for the data driven approaches of machine translation . Parallel corpus building is a labour intensive task , thus making it a costly and scarce resource . Full potential of available data needs to be exploited and this can be ensured by removing different types of inconsistencies as being faced throughout the NLP domain .
The construction of the parallel corpus facilitates the identification and extraction of English health risk terms which gave rise to competing translations or the detected terminological variation in health translation , the translation phenomenon highlighted in this chapter and discussed at length in this book . The variant translations extracted in the parallel corpus exploration were grouped into different clusters of health risk terminologies that underscored the development and building of new health risks knowledge among the target audiences .
Better Splitting Algorithms for Parallel Corpus Processing Each iteration of minimum error rate training involves re-translating a development set . Distributing this work across computational nodes can speed up translation time , but in practice some parts may take much longer to complete than others , leading to computational slack time .
A review of typical parallel FPM algorithms uncovers the major challenges , methodologies , and research problems in the field of parallel frequent pattern discovery , such as work-load balancing , finding good data layouts , and data decomposition . This survey also indicates a dramatic shift of the research interest in the field from the simple parallel frequent itemset mining on traditional parallel and distributed platforms to parallel pattern mining of more complex data on emerging architectures , such as multi-core systems and the increasingly mature grid infrastructure .
Second , we describe a combined anchor-point-based method for comparable sentences alignment . Third , we present a compositional-based approach for parallel phrase mining . We conducted multiple evaluations to qualify the extracted comparable and parallel data .
We also achieve very competitive results in cross - lingual document classification ( MLDoc dataset ) . Our sentence embeddings are also strong at parallel corpus mining , establishing a new state - of - the - art in the BUCC shared task for 3 of its 4 language pairs . Finally , we introduce a new test set of aligned sentences in 122 languages based on the Tatoeba corpus , and show that our sentence embeddings obtain strong results in multilingual similarity search even for low - resource languages .
Our experiments show large improvements over existing methods . We outperform the best published results on the BUCC shared task on parallel corpus mining by more than 10 F1 points . We also improve the precision from 48.9 to 83.3 on the reconstruction of 11.3 M English - French sentence pairs of the UN corpus .
Improved C4.5 Algorithm for the Analysis of Sales Descriptive Data Mining Modelling access to renal transplantation waiting list in a French healthcare network using a Bayesian method Analysis of Power Marketing Data Mining Based on the Big Data Technology Finding Diamonds in Data: Reflections on Teaching Data Mining from the Coal Face Parallel Data Mining from Multicore to Cloudy Grids All-Visible-k-Nearest-Neighbor Queries Применение технологий интеллектуального анализа данных в естественнонаучных, технических и гуманитарных областях Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data A Review on Artificial Intelligence and Data mining in Cyber security. Crop identification with wavelet packet analysis and weighted Bayesian distance A secure privacy preservation model for vertically partitioned distributed data Research on the Application of Data Mining Exploratory mining via constrained frequent set queries Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New Experiments Explicit Interaction Model towards Text Classification A genetic algorithm feature selection based approach for Arabic Sentiment Classification Spam Detection and Elimination of Messages from Twitter Emotion analysis of Twitter using opinion mining Optimizing Natural Language Processing Pipelines: Opinion Mining Case Study Twitter as a Corpus for Sentiment Analysis and Opinion Mining Social opinion mining and concise rendition Analysis on social influence and public awareness of Republic of Korea on creative education using opinion mining techniques Feature selection for ordinal regression Feature Selection for Ordinal Text Classification An Efficient Naive Bayes Classification for Sentiment Analysis on Twitter Social inclusion and digital divide: eParticipation dilemmas in municipalities Attending to Characters in Neural Sequence Labeling Models Effective LSTMs for Target-Dependent Sentiment Classification DialogueRNN: An Attentive RNN for Emotion Detection in Conversations Incremental Update Algorithm of Sequential Patterns Mining Based on Projected Datasets An experimental evaluation of two approaches to mining context based sequential patterns Constraint-based sequential pattern mining: the pattern-growth methods Abhinav National Monthly Refereed Journal of Research in Science & Technology DISCOVERING SEQUENTIAL PATTERNS IN SEQUENCE DATABASE USING CONSTRAINTS Mining Sequential Patterns With Consideration To Recency, Frequency, And Monetary A Comparative Study of GTC and PSP Algorithms for Mining Sequential Patterns Embedded in Database with Time Constraints Sampling for Sequential Pattern Mining: From Static Databases to Data Streams End-to-End Argument Mining for Discussion Threads Based on Parallel Constrained Pointer Architecture From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion Detecting logical argumentation in text via communicative discourse tree Is Something Better than Nothing? Automatically Predicting Stance-based Arguments Using Deep Learning and Small Labelled Dataset A Framework for Detection and Identification the Components of Arguments in Arabic Legal Texts A Cascade Model for Proposition Extraction in Argumentation Argumentation mining: How can a machine acquire common sense and world knowledge? Combining Deep Learning and Argumentative Reasoning for the Analysis of Social Media Textual Content Using Small Data Sets Extracting Argument and Domain Words for Identifying Argument Components in Texts DialogueRNN: An Attentive RNN for Emotion Detection in Conversations Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora Bracketing and aligning words and constituents in parallel text using Stochastic Inversion Transduction Grammars ‘Be being + 서술 형용사’ 구문의 영한번역전략 Stochastic Inversion Transduction Grammars, with Application to Segmentation, Bracketing, and Alignment of Parallel Corpora ТАКСОНОМИЯ И ОПИСАНИЕ ПОКАЗАТЕЛЕЙ НЕОПРЕДЕЛЕННОСТИ В РУССКОМ ЯЗЫКЕ НА ОСНОВЕ ДАННЫХ ПАРАЛЛЕЛЬНОГО КОРПУСА Pre-processing English-Hindi Corpus for Statistical Machine Translation Construction of an English-Chinese Parallel Corpus of WHO Health Translation Better Splitting Algorithms for Parallel Corpus Processing Parallel Frequent Pattern Discovery: Challenges and Methodology Building and Exploiting Domain-Specific Comparable Corpora for Statistical Machine Translation Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
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MIE High Performance Computing Workshop DEXA (2) SIGMOD Conference ArXiv AAAI AICCSA IC3 CIARP LREC SAC ICEGOV COLING COLING AAAI PACIS ICDM ACL (1) NAACL-HLT (2) ArgMining@ACL ArgMining@HLT-NAACL AAAI IJCAI Transactions of the Association for Computational Linguistics ACL
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