A decision tree using ID3 algorithm for English semantic analysis

Phu, Vo Ngoc; Tran, Vo Thi Ngoc; Chau, Vo Thi Ngoc; Dat, Nguyen Duy; Duy, Khanh Ly Doan

doi:10.1007/s10772-017-9429-x

Cited by 42 publications

(21 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

Self Cite

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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

Self Cite

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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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“…In order to solve the variety bias problem, different weights are introduced into each attribution based on Equation (17). Each weight equals the reciprocal of the length of different values in the corresponding attribution.…”

Section: The Selection Process Of the Optimal Attribution Based On Thmentioning

confidence: 99%

“…A large amount of decision tree algorithms such as Iterative Dichotomizer 3 (ID3) [17], C4.5 [18], and Classification And Regression Tree (CART) [19] are used in different fields. Most decision tree methods are developed from the ID3 method.…”

Section: Introductionmentioning

confidence: 99%

Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree

Wang

Song

et al. 2017

Algorithms

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Abstract:The decision tree algorithm is a core technology in data classification mining, and ID3 (Iterative Dichotomiser 3) algorithm is a famous one, which has achieved good results in the field of classification mining. Nevertheless, there exist some disadvantages of ID3 such as attributes biasing multi-values, high complexity, large scales, etc. In this paper, an improved ID3 algorithm is proposed that combines the simplified information entropy based on different weights with coordination degree in rough set theory. The traditional ID3 algorithm and the proposed one are fairly compared by using three common data samples as well as the decision tree classifiers. It is shown that the proposed algorithm has a better performance in the running time and tree structure, but not in accuracy than the ID3 algorithm, for the first two sample sets, which are small. For the third sample set that is large, the proposed algorithm improves the ID3 algorithm for all of the running time, tree structure and accuracy. The experimental results show that the proposed algorithm is effective and viable.

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A decision tree using ID3 algorithm for English semantic analysis

Cited by 42 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

Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree

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