Semantic lexicons of English nouns for classification

Phu, Vo Ngoc; Tran, Vo Thi Ngoc; Chau, Vo Thi Ngoc; Duy, Dat Nguyen

doi:10.1007/s12530-017-9188-6

Cited by 15 publications

(7 citation statements)

References 35 publications

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“…Unsupervised classification has been investigated through numerous means (Turney, 2002;Lee et al, 2002a;van Zyl, 2002;Hegarat-Mascle et al, 2002;Ferro-Famil et al, 2002;Chaovalit and Zhou, 2005;Lee et al, 2002b;Gllavata et al, 2004;Phu et al, 2016;Phu et al, 2017a;2017b;2017c;2017d;2017e;2017f;2017g;2017h), such as a simple unsupervised learning algorithm to classify reviews as recommended or not recommended, which classified reviews according to the average semantic orientation of the phrases, specifically the adjectives and adverbs, used in the reviews (Hegarat-Mascle et al, 2002). In another study , the use of an imaging radar polarimeter data for unsupervised classification of scattering behavior was described by comparing the polarization properties of each pixel in an image to that of simple classes of scattering, such as even number of reflections, odd number of reflections, and diffuse scattering (van Zyl, 2002).…”

Section: Related Workmentioning

confidence: 99%

A CURE Algorithm for Vietnamese Sentiment Classification in a Parallel Environment

Phu¹,

Tran²,

Max³

2019

Journal of Computer Science

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Solutions to process big data are imperative and beneficial for numerous fields of research and commercial applications. Thus, a new model has been proposed in this paper to be used for big data set sentiment classification in the Cloudera parallel network environment. Clustering Using Representatives (CURE), combined with Hadoop MAP (M)/REDUCE (R) in Clouderaa parallel network system, was used for 20,000 documents in a Vietnamese testing data set. The testing data set included 10,000 positive Vietnamese documents and 10,000 negative ones. After testing our new model on the data set, a 62.92% accuracy rate of sentiment classification was achieved. Although our data set is small, this proposed model is able to process millions of Vietnamese documents, in addition to data in other languages, to shorten the execution time in the distributed environment.

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Section: Related Workmentioning

confidence: 99%

A CURE Algorithm for Vietnamese Sentiment Classification in a Parallel Environment

Phu¹,

Tran²,

Max³

2019

Journal of Computer Science

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“…According to [1,[13][14][15][16][17][18], Pointwise Mutual Information (PMI) between two words wi and wj has the equation P M I(w i , w j ) = log 2 P (w i , w j ) P (w i ) × P (w j )…”

Section: Creating the Sentiment Lexicons Of The Besdmentioning

confidence: 99%

Latent Semantic Analysis using a Dennis Coefficient for English Sentiment Classification in a Parallel System

Ngoc

2018

INT J COMPUT COMMUN

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Abstract:We have already survey many significant approaches for many years because there are many crucial contributions of the sentiment classification which can be applied in everyday life, such as in political activities, commodity production, and commercial activities. We have proposed a novel model using a Latent Semantic Analysis (LSA) and a Dennis Coefficient (DNC) for big data sentiment classification in English. Many LSA vectors (LSAV) have successfully been reformed by using the DNC. We use the DNC and the LSAVs to classify 11,000,000 documents of our testing data set to 5,000,000 documents of our training data set in English. This novel model uses many sentiment lexicons of our basis English sentiment dictionary (bESD). We have tested the proposed model in both a sequential environment and a distributed network system. The results of the sequential system are not as good as that of the parallel environment. We have achieved 88.76% accuracy of the testing data set, and this is better than the accuracies of many previous models of the semantic analysis. Besides, we have also compared the novel model with the previous models, and the experiments and the results of our proposed model are better than that of the previous model. Many different fields can widely use the results of the novel model in many commercial applications and surveys of the sentiment classification.

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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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Semantic lexicons of English nouns for classification

Cited by 15 publications

References 35 publications

A CURE Algorithm for Vietnamese Sentiment Classification in a Parallel Environment

A CURE Algorithm for Vietnamese Sentiment Classification in a Parallel Environment

Latent Semantic Analysis using a Dennis Coefficient for English Sentiment Classification in a Parallel System

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

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