An Upgrading Feature-Based Opinion Mining Model on Vietnamese Product Reviews

Ha, Quang-Thuy; Vu, Tien-Thanh; Pham, Huyen-Trang; Luu, Cong-To

doi:10.1007/978-3-642-23620-4_21

Cited by 14 publications

(11 citation statements)

References 14 publications

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“…Much recent research has addressed sentiment classification (Manek et al, 2016;Agarwal and Mittal, 2016a;2016b;Canuto et al, 2016;Ahmed and Danti, 2016;Phu and Tuoi, 2014;Tran et al, 2014;Phu et al, 2016;Phu et al, 2017a;2017b) such as a Gini-Indexbased feature selection method with an SVM classifier for a large movie review data set (Manek et al, 2016), as well as a corpus-based semantic orientation approach for sentiment analysis (Agarwal and Mittal, 2016b). Furthermore, several studies have recently investigated Vietnamese sentiment classification (Ha et al, 2011;Bang et al, 2015;Kieu and Pham, 2010;Vu and Park, 2014;Hoanh-Su et al, 2015;Anh and Dau, 2014;Phan and Cao, 2014;Duyen et al, 2014;Bach et al, 2015;Trinh et al, 2016). Researchers have proposed various methods to address such data sets, including an upgrading FOMS model on Vietnamese reviews on mobile phone products (Ha et al, 2011) and an improved technique to analyze sentiment for Vietnamese texts based on the term feature selection approach (Bang et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, several studies have recently investigated Vietnamese sentiment classification (Ha et al, 2011;Bang et al, 2015;Kieu and Pham, 2010;Vu and Park, 2014;Hoanh-Su et al, 2015;Anh and Dau, 2014;Phan and Cao, 2014;Duyen et al, 2014;Bach et al, 2015;Trinh et al, 2016). Researchers have proposed various methods to address such data sets, including an upgrading FOMS model on Vietnamese reviews on mobile phone products (Ha et al, 2011) and an improved technique to analyze sentiment for Vietnamese texts based on the term feature selection approach (Bang et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

“…Our research Our model's merits and demerits are illustrated in the Conclusion section. Table 8: Comparisons of our model with the latest Vietnamese sentiment classification models (or the latest Vietnamese sentiment classification methods) in (Ha et al, 2011;Bang et al, 2015;Kieu and Pham, 2010;Xuan-Son Vu and Park, 2014;Le et al, 2015;Trinh and Dau, 2014;Hoanh-Su et al, 2015;Phan and Cao, 2014;Duyen et al, 2014;Bach et al, 2015;Son Trinh et al, 2016) Studies CA SC L SD DT PNE Approach (Ha et al, 2011) No Yes VL Yes Yes No +HAC clustering +Semi-supervised SVM-kNN classification (Bang et al, 2015) No Yes VL Yes Yes No +Decision Tree +Naive Bayes (NB) +Support Vector Machines (SVM +Feature selection technique, χ2 (CHI). (Kieu and Pham, 2010) No Yes VL Yes Yes No A rule-based system using the Gate framework (Vu and Park, 2014) No Yes VL No No No A method to construct VSWN from a Vietnamese dictionary, not from WordNet.…”

Section: Our Studymentioning

confidence: 99%

“…Our model is quite different from other studies related to Vietnamese vectors, Vietnamese segments, etc. (Hoang et al, 2007;Le et al, 2008;Nguyen et al, 2009), research related to the CURE algorithm (Guha et al, 1998;Yan-Hua et al, 2011;Nian-yun et al, 2009;Ertöz et al, 2002;Kaya and Alhajj, 2005;Ying et al, 2008;Rani et al, 2014), recent studies about Vietnamese sentiment classification (Ha et al, 2011;Bang et al, 2015;Kieu and Pham, 2010;Vu and Park, 2014;Hoanh-Su et al;Anh and Dau, 2014;Phan and Cao, 2014;Duyen et al, 2014;Bach et al, 2015;Trinh et al, 2016), sentiment classification (Manek et al, 2016;Agarwal and Mittal, 2016a;2016b;Canuto et al, 2016;Ahmed and Danti, 2016;Phu and Tuoi, 2014;Tran et al, 2014;Phu et al, 2016;Phu et al, 2017a;2017b) and unsupervised classification (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).…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Related Workmentioning

confidence: 99%

Section: Our Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A CURE Algorithm for Vietnamese Sentiment Classification in a Parallel Environment

Phu¹,

Tran²,

Max³

2019

Journal of Computer Science

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“…Jia et al [62] reported 71% precision by employing K-means clustering on Chinese language reviews about products. Ha et al [60] used a K-means approach on mobile phone reviews and reported 72% accuracy.…”

mentioning

confidence: 99%

Spam Review Detection Techniques: A Systematic Literature Review

et al. 2019

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Online reviews about the purchase of products or services provided have become the main source of users’ opinions. In order to gain profit or fame, usually spam reviews are written to promote or demote a few target products or services. This practice is known as review spamming. In the past few years, a variety of methods have been suggested in order to solve the issue of spam reviews. In this study, the researchers carry out a comprehensive review of existing studies on spam review detection using the Systematic Literature Review (SLR) approach. Overall, 76 existing studies are reviewed and analyzed. The researchers evaluated the studies based on how features are extracted from review datasets and different methods and techniques that are employed to solve the review spam detection problem. Moreover, this study analyzes different metrics that are used for the evaluation of the review spam detection methods. This literature review identified two major feature extraction techniques and two different approaches to review spam detection. In addition, this study has identified different performance metrics that are commonly used to evaluate the accuracy of the review spam detection models. Lastly, this work presents an overall discussion about different feature extraction approaches from review datasets, the proposed taxonomy of spam review detection approaches, evaluation measures, and publicly available review datasets. Research gaps and future directions in the domain of spam review detection are also presented. This research identified that success factors of any review spam detection method have interdependencies. The feature’s extraction depends upon the review dataset, and the accuracy of review spam detection methods is dependent upon the selection of the feature engineering approach. Therefore, for the successful implementation of the spam review detection model and to achieve better accuracy, these factors are required to be considered in accordance with each other. To the best of the researchers’ knowledge, this is the first comprehensive review of existing studies in the domain of spam review detection using SLR process.

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