Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm

Nurcahyawati, Vivine; Mustaffa, Zuriani

doi:10.11591/eei.v12i3.4830

Cited by 11 publications

(17 citation statements)

References 25 publications

(94 reference statements)

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“…Afterward, dimensionality reduction was performed using FM and TF-IDF techniques. The classification stage employed the SVM algorithm due to its demonstrated high performance in previous studies [14]. Analysis through these stages showed that customer orientation tended to be positive with a value of 76%.…”

Section: Discussionmentioning

confidence: 99%

“…The annotation process involves linguists who determine the sentiment orientation of each sentence, with 1 indicating a negative review and 2 for a positive one. It is essential to note that this process is both time-consuming and costly [14]. Some data points (examples in Table 1) may not be suitable for manual annotation, as mentioned in Section 2.…”

Section: F Measurement and Evaluationmentioning

confidence: 99%

“…Several studies have shown the efficacy of Support Vector Machine (SVM) in customer orientation analysis, outperforming other algorithms. Specifically, when combined with Fuzzy Matching (FM), this algorithm has consistently produced superior results across multiple datasets [14]. Studies have found that SVM with RBF kernel outperforms the Sigmoid kernel in terms of accuracy [15].…”

Section: Introductionmentioning

confidence: 99%

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Vader Lexicon and Support Vector Machine Algorithm to Detect Customer Sentiment Orientation

Nurcahyawati¹,

Mustaffa

2023

J. Inf. Syst. Eng. Bus. Intell.

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Background: The concept of customer orientation, which is based on a set of fundamental beliefs that prioritize the interests of the customer, requires companies to detect these interests in order to maintain a high level of quality in their products or services. Furthermore, there are several indicators of customer orientation, and one of them is their opinion or taste, which provides valuable feedback for businesses. With the rapid development of social media, customers can express emotions, thoughts, and opinions about services or products that may not be easily conveyed in the real world. Objective: The objective of this study is to detect customer orientation towards product or service quality, as expressed in online or social media. Additionally, the study showcases the novelty and superiority of the annotation process used for detecting customer orientation classifications. Methods: This study employs a method to compare the classification performance of the Vader lexicon annotation process with manual annotation. To accomplish this, a dataset from the Amazon website will be analyzed and classified using the Support Vector Machine algorithm. The objective of this method is to determine the level of customer orientation present within the dataset. To evaluate the effectiveness of the Vader lexicon, the study will compare the results of manual and automatic data annotation. Results: The results showed that customer orientation towards product or service quality has a predominantly positive value, comprising up to 76% of the total responses analyzed. Conclusion: The findings demonstrate that using Vader in the annotation process results in superior accuracy values compared to manual annotation. Specifically, the accuracy value increased from 86% to 88.57%, indicating that Vader could be a reliable tool for annotating text. Therefore, future studies should consider using Vader as a classifier or integrating it into the annotation process to further enhance its performance. Keywords: Classification, Customer, Orientation, Text analysis, Vader lexicon,

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Section: Discussionmentioning

confidence: 99%

Section: F Measurement and Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vader Lexicon and Support Vector Machine Algorithm to Detect Customer Sentiment Orientation

Nurcahyawati¹,

Mustaffa

2023

J. Inf. Syst. Eng. Bus. Intell.

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“…Text classification is assigning documents to predefined classes. Web page classification [1,2], sentiment classification [3][4][5], customer complaints classification [6,7], spam detection [8,9], tweet classification [10,11] and other classifications [6,[12][13][14][15][16][17][18] are samples of text classification in digital environment.…”

Section: Introductionmentioning

confidence: 99%

Effects of Preprocessing on Text Classification in Balanced and Imbalanced Datasets

2024

KSII TIIS

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In this study, preprocessings with all combinations were examined in terms of the effects on decreasing word number, shortening the duration of the process and the classification success in balanced and imbalanced datasets which were unbalanced in different ratios. The decreases in the word number and the processing time provided by preprocessings were interrelated. It was seen that more successful classifications were made with Turkish datasets and English datasets were affected more from the situation of whether the dataset is balanced or not. It was found out that the incorrect classifications, which are in the classes having few documents in highly imbalanced datasets, were made by assigning to the class close to the related class in terms of topic in Turkish datasets and to the class which have many documents in English datasets. In terms of average scores, the highest classification was obtained in Turkish datasets as follows: with not applying lowercase, applying stemming and removing stop words, and in English datasets as follows: with applying lowercase and stemming, removing stop words. Applying stemming was the most important preprocessing method which increases the success in Turkish datasets, whereas removing stop words in English datasets. The maximum scores revealed that feature selection, feature size and classifier are more effective than preprocessing in classification success. It was concluded that preprocessing is necessary for text classification because it shortens the processing time and can achieve high classification success, a preprocessing method does not have the same effect in all languages, and different preprocessing methods are more successful for different languages.

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“…Ahmed et al [9] proposed a hybrid model based on simulated annealing (SA) and generalized normal distribution optimizer (GNDO). Nurcahyawati et al [10] proposed a comment classification model based on SVM. Parlak et al [11] proposed a text classification feature filtering method based on machine learning.…”

Section: Introductionmentioning

confidence: 99%

Six-Granularity Based Chinese Short Text Classification

2023

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Short text classification is an important task in Natural Language Processing (NLP). The classification result for Chinese short text is always not ideal due to the sparsity problem of them. Most of the previous classification models for Chinese short text are based on word or character, considering that Chinese radical can also represent the meaning individually, so word, character and radical are all used to build a Chinese short text classification model in this paper, which solves the data sparsity problem of short text. In addition, in the process of segmenting sentences into words, considering that jieba will cause the loss of key information and ngram will generate noise words, both jieba and ngram are used to construct a six-granularity (i.e. word-jieba, word-jieba-radical, word-ngram, word-ngram-radical, character and character-radical) based Chinese short text classification (SGCSTC) model. Additionally, different weights are assigned to the six granularities and are automatically updated in the process of backpropagation using cross-entropy loss due to the different influence of them on the classification results. The classification Accuracy, Precision, Recall and F1 of SGCSTC in THUCNews-S dataset are 93.36%, 94.47%, 94.15% and 94.31% respectively, and that in CNT dataset are 92.67%, 92.38%, 93.15% and 92.76% respectively, and multiple comparative experiment results on THUCNews-S and CNT datasets show that SGCSTC outperforms the state-of-the-art text classification models.

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Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm

Cited by 11 publications

References 25 publications

Vader Lexicon and Support Vector Machine Algorithm to Detect Customer Sentiment Orientation

Vader Lexicon and Support Vector Machine Algorithm to Detect Customer Sentiment Orientation

Effects of Preprocessing on Text Classification in Balanced and Imbalanced Datasets

Six-Granularity Based Chinese Short Text Classification

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