Opinion mining on book review using CNN-L2-SVM algorithm

Rozi, Muhammad Fahrur; Mukhlash, Imam; Soetrisno,; Kimura, Masaomi

doi:10.1088/1742-6596/974/1/012004

Cited by 3 publications

(5 citation statements)

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“…The result obtained an accuracy of 60%. So, our method outperforms the methods are used by Rozi et al [6] and Taspinar [7].…”

Section: Discussionmentioning

confidence: 77%

“…This is a common phenomenon in classification using artificial neural networks. In the research of Rozi et al [6] that use a combination of methods of Convolutional Neural Network with Support Vector Machine to classify the same data. The result obtained a smaller error than those who do not use Support Vector Machine with a result of 64.6% for testing data and 83.23% for training data.…”

Section: Resultsmentioning

confidence: 99%

“…This method produces the class type of an opinion into two main classes: positive and negative. The CNN method is the same as Kim's research [5] but the activation function is the same as in Long Chen [6] and Jin Wang [4]. This method is applied on book review data obtained from Amazon.com's online site.…”

Section: Introductionmentioning

confidence: 99%

“…This method is applied on book review data obtained from Amazon.com's online site. Fakhrur Rozi et al [6] discussed opinion mining using CNN-L2-SVM algorithm. Taspinar described about sentiment analysis with bag-of-words [7].…”

Section: Introductionmentioning

confidence: 99%

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Opinion Mining on Book Review Using Convolutional Neural Network Algorithm — Long Short Term Memory

Mukhlash

Arham

Rozi

et al. 2018

IJMLC

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“…The result obtained an accuracy of 60%. So, our method outperforms the methods are used by Rozi et al [6] and Taspinar [7].…”

Section: Discussionmentioning

confidence: 77%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Opinion Mining on Book Review Using Convolutional Neural Network Algorithm — Long Short Term Memory

Mukhlash

Arham

Rozi

et al. 2018

IJMLC

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“…In contrast, this paper proposes a new method called OMLML that is usable in various applications and fields. In comparison with methods using just machine learning or lexicon to mine opinions such as Puri et al (2018), Bhatnagar et al (2018), Tudoran (2018), Mostafa (2018), Karami et al (2018), Yun et al (2018), Narayan et al (2018), Nuortimo and Härkönen (2018), Souza et al (2018), Rozi et al (2018), Akhmedova et al (2018), Solanki et al (2019) and Kang et al (2018), the method applied for users' opinions mining in this paper combines a machine learning-based method and a lexicon-based method in order to classify opinions and sentiments with more accuracy. In addition, in this paper to create a model of mining based on machine learning and to improve accuracy and performance of the opinion mining method, an improved neural-fuzzy network is proposed.…”

Section: Tablementioning

confidence: 99%

OMLML: a helpful opinion mining method based on lexicon and machine learning in social networks

Keyvanpour

Zandian

Heidarypanah

2020

Soc. Netw. Anal. Min.

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Identification of users' polarities and mining their opinions in various areas, especially social networks, has become one of the popular and useful research fields. Although opinion mining and analyzing methods based on machine learning or lexicon have been useful, high training cost based on time or memory used, lack of enriched and complete lexicons, high dimensions of feature space and ambiguity in positive or negative detection of some sentences in these methods are examples of their downsides. To cope with these problems, in this paper a helpful method based on lexicon and machine learning called OMLML is proposed by using social networks. The main superiority of the proposed method compared to other methods is addressing these challenges simultaneously. According to the proposed method, the polarity of the opinions toward a target word is first determined using a method based on lexicon and textual features of words and sentences. Next, having mapped feature space into a 3-D vector, opinions are analyzed and classified based on a new machine learning method. The results of quantitative and qualitative experiments show that mapping data into a new space decreases training cost and that the performance of the proposed method than is acceptable particularly from the perspective of accuracy, F-measure and runtime.

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Indonesian Finance News Sentiment from Hybrid Deep Learning and Support Vector Machine

Mukhlash

Gama

Iqbal

et al. 2022

2022 5th International Conference on Mathematics and Statistics

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Opinion mining on book review using CNN-L2-SVM algorithm

Cited by 3 publications

References 1 publication

Opinion Mining on Book Review Using Convolutional Neural Network Algorithm — Long Short Term Memory

Opinion Mining on Book Review Using Convolutional Neural Network Algorithm — Long Short Term Memory

OMLML: a helpful opinion mining method based on lexicon and machine learning in social networks

Indonesian Finance News Sentiment from Hybrid Deep Learning and Support Vector Machine

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