|
ISSN
| 1407-5806 | |
DDC
| 004 | |
Tác giả CN
| Vo, Ngoc Phu | |
Nhan đề
| K-Medoids algorithm used for English sentiment
classification in a distributed system / Vo Ngoc Phu, Vo Thi Ngoc Tran | |
Thông tin xuất bản
| Latvia : Latvian Transport Development and Education Association, 2018 | |
Mô tả vật lý
| 20 p. | |
Tóm tắt
| 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 K-Medoids Algorithm (PAM) with multi-dimensional 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 85.98% accuracy. | |
Thuật ngữ chủ đề
| Bigdata-Algorithm, English sentiment | |
Từ khóa tự do
| Computer and information technologies | |
Từ khóa tự do
| Mathematical and computer modelling | |
Từ khóa tự do
| Natural and engineering sciences | |
Từ khóa tự do
| Operation research and decision making | |
Khoa
| Khoa Công nghệ Thông tin | |
Tác giả(bs) CN
| Vo, Thi Ngoc Tran | |
Nguồn trích
| Computer Modelling And New Technologies.
Số: Vol. 22 (2018), P.20-39, , | |
Địa chỉ
| Thư Viện Đại học Nguyễn Tất Thành |
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| 044 | |alv |
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| 082 | |a004|223 |
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| 100 | |aVo, Ngoc Phu |
|---|
| 245 | |aK-Medoids algorithm used for English sentiment
classification in a distributed system / |cVo Ngoc Phu, Vo Thi Ngoc Tran |
|---|
| 260 | |aLatvia : |bLatvian Transport Development and Education Association, |c2018 |
|---|
| 300 | |a20 p. |
|---|
| 520 | |aSentiment 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 K-Medoids Algorithm (PAM) with multi-dimensional 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 85.98% accuracy. |
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| 650 | |aBigdata|vAlgorithm, English sentiment |
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| 653 | |aComputer and information technologies |
|---|
| 653 | |aMathematical and computer modelling |
|---|
| 653 | |aNatural and engineering sciences |
|---|
| 653 | |aOperation research and decision making |
|---|
| 690 | |aKhoa Công nghệ Thông tin |
|---|
| 700 | |aVo, Thi Ngoc Tran |
|---|
| 773 | |tComputer Modelling And New Technologies|gVol. 22 (2018), P.20-39 |
|---|
| 852 | |aThư Viện Đại học Nguyễn Tất Thành |
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| 890 | |c1|a0|b0|d1 |
|---|
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