A C4.5 algorithm for english emotional classification

Ngoc, Phuong Pham; Ngoc, Chau Vo Thi; Ngoc, Tran Vo Thi; Duy, Dat Nguyen

doi:10.1007/s12530-017-9180-1

Cited by 41 publications

(32 citation statements)

References 40 publications

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“…-The advantages and disadvantages of the proposed model are shown in the Conclusion section. (Agarwal and Mittal, 2016a;2016b;Canuto et al, 2016;Ahmed and Danti, 2016;Phu and Tuoi, 2014;Tran et al, 2014;Dat et al, 2017;Phu et al, 2017f;2017g;2017h) Studies Approach Positives Negatives Agarwal and Mittal (2016a) The Machine Learning The main emphasis of this survey is to No mention Approaches Applied to discuss the research involved in Sentiment Analysis-Based applying machine learning methods, Applications mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this study for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection and (4) machine-learning methods.…”

Section: Resultsmentioning

confidence: 99%

“…We also compare this novel model's results with the latest sentiment classification models in (Agarwal and Mittal, 2016a;2016b;Canuto et al, 2016;Ahmed and Danti, 2016;Phu and Tuoi, 2014;Tran et al, 2014;Dat et al, 2017;Phu et al, 2017f;2017g;2017h) This study contains 6 sections. Section 1introduces the study; section 2 discusses the related works about the JOHNSON Coefficient (JC), etc.…”

Section: Introductionmentioning

confidence: 99%

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English Sentiment Classification using Only the Sentiment Lexicons with a JOHNSON Coefficient in a Parallel Network Environment

Phu¹,

Tran²

2018

American Journal of Engineering and Applied Sciences

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Sentiment classification is significant in everyday life, such as in political activities, commodity production and commercial activities. In this survey, we have proposed a new model for Big Data sentiment classification. We use many sentiment lexicons of our basis English Sentiment Dictionary (bESD) to classify 5,000,000 documents including 2,500,000 positive and 2,500,000 negative of our testing data set in English. We do not use any training data set in English. We do not use any onedimensional vector in both a sequential environment and a distributed network system. We also do not use any multi-dimensional vector in both a sequential system and a parallel network environment. We use a JOHNSON Coefficient (JC) through a Google search engine with AND operator and OR operator to identify many sentiment values of the sentiment lexicons of the bESD in English. One term (a word or a phrase in English) is clustered into either the positive polarity or the negative polarity if this term is very close to either the positive or the negative by using many similarity measures of the JC. It means that this term is very similar to either the positive or the negative. We tested the proposed model in both a sequential environment and a distributed network system. We achieved 87.56% accuracy of the testing data set. The execution time of the model in the parallel network environment is faster than the execution time of the model in the sequential system. Our new model can classify sentiment of millions of English documents based on the sentiment lexicons of the bESD in a parallel network environment. The proposed model is not depending on both any special domain and any training stage. This survey used many similarity coefficients of a data mining field. The results of this work can be widely used in applications and research of the English sentiment classification.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

English Sentiment Classification using Only the Sentiment Lexicons with a JOHNSON Coefficient in a Parallel Network Environment

Phu¹,

Tran²

2018

American Journal of Engineering and Applied Sciences

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“…The latest researches of the sentiment classification are (Agarwal and Mittal, 2016a;2016b;Canuto et al, 2016;Ahmed and Danti, 2016;Phu and Tuoi, 2014;Tran et al, 2014;Phu et al, 2017a;Dat et al, 2017;Phu et al, 2016;2017b;2017c;2017d;2017e;2017f;2017g;2017h;2017i;2017j). In the research Lin et al (2007b), the authors present their machine learning experiments with regard to sentiment analysis in blog, review and forum texts found on the World Wide Web and written in English, Dutch and French.…”

Section: Related Workmentioning

confidence: 99%

A STING Algorithm and Multi-dimensional Vectors Used for English Sentiment Classification in a Distributed System

Phu¹,

Tran²

2018

American Journal of Engineering and Applied Sciences

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Sentiment classification is significant in everyday life, such as in political activities, commodity production and commercial activities. Finding a fast, highly accurate solution to classify emotion has been a challenge for scientists. In this research, we have proposed a new model for Big Data sentiment classification in the parallel network environment -a Cloudera system with Hadoop Map (M) and Hadoop Reduce (R). Our new model has used a Statistical Information Grid Algorithm (STING) with multidimensional vector and 2,000,000 English documents of our English training data set for English document-level sentiment classification. Our new model can classify sentiment of millions of English documents based on many English documents in the parallel network environment. However, we tested our new model on our testing data set (including 1,000,000 English reviews, 500,000 positive and 500,000 negative) and achieved 83.92% accuracy.

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“…(10) We used a RC through a Google search engine with AND operator and OR operator to identify many sentiment values and polarities of the sentiment lexicons in English. (11) We used the SOM to classify one document of the testing data set -TES into either the positive polarity or the negative polarity. (12) The input of this survey is the documents of the TES and the sentences of the training data set -TRA in English.…”

Section: Introductionmentioning

confidence: 99%

“…(10) The Cloudera distributed environment is used in this study. (11) The proposed work can be applied to other distributed systems. (12) This survey uses M and R. (13) Our proposed model can be applied to many different parallel network environments such as a Cloudera system.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Classification in English Using a Self-Organizing Map Algorithm with Only the One-Dimensional Vectors and a Ruzicka Coefficient in a Parallel System

Ngoc¹,

Ngoc²

2018

IJIES

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Sentiment classification has been used in many different fields because it has many significant contributions in everyday life, such as in political activities, commodity production, and commercial activities. We have proposed a new model for big data sentiment classification by using a combination of an unsupervised learning algorithm of a machine learning with a Ruzicka Coefficient (RC) in this work. A Self-Organizing Map Algorithm (SOM) of the machine learning is used in clustering the documents of the testing data set (TES) comprising 7,500,000 documents, which are the 3,750,000 positive and the 3,750,000 negative in English, into either the positive group or the negative group of our training data set (TRA) which is 3,000,000 sentences including the 1,500,000 positive sentences and the 1,500,000 negative sentences in English. In this study, we do not use a vector space modeling (VSM). We do not use any multi-dimensional vectors according to both the VSM and many sentiment lexicons. We use many sentiment lexicons of our basis English sentiment dictionary (bESD). We use many one-dimensional vectors based on the sentiment lexicons. We use a similarity coefficient in this study. We do not use any one-dimensional vectors based on the VSM. We have achieved 88.64% accuracy of the TES. The execution time of the proposed model in a distributed network environment-DNE is less than that in a sequential system-SS. Many commercial applications and surveys of the sentiment classification can widely use the results of the proposed model.

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A C4.5 algorithm for english emotional classification

Cited by 41 publications

References 40 publications

English Sentiment Classification using Only the Sentiment Lexicons with a JOHNSON Coefficient in a Parallel Network Environment

English Sentiment Classification using Only the Sentiment Lexicons with a JOHNSON Coefficient in a Parallel Network Environment

A STING Algorithm and Multi-dimensional Vectors Used for English Sentiment Classification in a Distributed System

Sentiment Classification in English Using a Self-Organizing Map Algorithm with Only the One-Dimensional Vectors and a Ruzicka Coefficient in a Parallel System

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