Sentiment Analysis on User Satisfaction Level of Mobile Data Services Using Support Vector Machine (SVM) Algorithm

Chory, Rimba Nuzulul; Nasrun, Muhammad; Setianingsih, Casi

doi:10.1109/iotais.2018.8600884

Cited by 14 publications

(8 citation statements)

References 10 publications

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“…Thus, this study ascertains support vector machine as a robust sentiment classifier in the banking context. This result is consistent with the findings of Adamu et al 2021;Xing et al 2020;Chory et al 2018;Istia & Purnomo, 2018;Al-Smadi et al 2018;Alayba et al 2017;Tan & Zhang, 2008 that showed SVM as the state-of-the-art method for sentiment analysis. To answer the research question in this study.…”

Section: Parameter Tuning and Feature Selection For ML Modelssupporting

confidence: 92%

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Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

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Banking is an essential component of our day-to-day activity, and the sector contributes to the development of every Nation's economy. To ensure bank stability, the banks have gone through several deregulation and reformation processes. Unfortunately, these processes and technological advancement have led to increased competitiveness, saturated market, and low profitability. Thus, the need for banks to build customer centric service to gain profitability and stability is vital. This opens the need to investigate customers attitude towards banking.With the growing usage of social network sites (SNS), the user generated content (UGC) has opened opportunities for banks to mine customers opinion . This can help the banks to generate insight into their product and services, create marketing strategies and manage their reputation. To the customers, this serves as source to information that can help in supporting decision making on product or service purchase. In this study, sentiment analysis (SA) techniques were employed to investigate customers attitude towards banking us ing Twitter data. However, the unstructured nature of the data and word ambiguity made sentiment analysis complicated. In the context of this study, SA is more difficult because it involves the natural language processing of Pidgin English and English words in the bank domain.Unfortunately, there are limited or no resources for this purpose.

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Section: Parameter Tuning and Feature Selection For ML Modelssupporting

confidence: 92%

“…Support vector machine (SVM) was proposed by Vapnik in the 1990s (Vapnik, 2013) and has been shown to be a reliable and well performed supervised learning mod el in sentiment analysis application (Chory et al 2018).…”

Section: Support Vector Machinementioning

confidence: 99%

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

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“…Machine learning classifiers use supervised approach and need training examples which can be labeled manually or obtained from online sources. naive bayes (NB) [37], support vector machines [38], [39], decision tree [40], [41], AdaBoost, regression logistic regression, J48, Simple CART, random tree are some commonly used machine learning based classifiers. Kolchyna et al [36] analyzed various machine learning classifiers.…”

Section: Machine Learning Classification Techniquesmentioning

confidence: 99%

Analyzing impact of number of features on efficiency of hybrid model of lexicon and stack based ensemble classifier for twitter sentiment analysis using WEKA tool

Rani¹,

Gill²,

Gulia³

2021

IJEECS

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<span>Twitter is used by millions of people across the world, so the data collected from Twitter can be highly valuable for research and helpful in decision support. Here in this paper ‘Twitter US Airline data’ from Kaggle data repository is used for sentiment classification of customers’ reviews. The current research aims to implement various machine learning classifiers, Stack-based ensemble classifiers and hybrid of lexicon classifier with other classifiers. 11 different classification models are implemented for different sized feature sets. Also, all the 11 models are re-implemented by adding sentiment score of lexicon based classifier as one of the features in the feature set. Results are analyzed by varying number of input feature variables used in the classification. Four different size feature sets having 301,501, 701, and 1301 number of features are used to analyze the variations in the final findings. Chi-Square and Information gain techniques are used for feature selection. The results show that an increase in the number of features increases the accuracy up to 701 features. After that, accuracy is stable or decreases with increase in feature set size. Also, the cost of adding sentiment score of lexicon classifier to the input feature set is nominal, but the results are improved consistently. WEKA and R Studio tools are used for analysis and implementation. Accuracy and Kappa are used for representing and comparing the efficiency of models.</span>

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“…S. Naz et al [35] used SVM for the classification of Twitter Data by using three weighted schemes to see the effect of different weighted schemes on accuracy of SVM. R. N. Chory et al [36] identified user satisfaction level of mobile data services by using SVM and a very high accuracy of 99.01 percent was monitored.…”

Section: Classification Algorithms Used In Ensemblementioning

confidence: 99%

Hybrid Model using Stack-Based Ensemble Classifier and Dictionary Classifier to Improve Classification Accuracy of Twitter Sentiment Analysis

Rani¹

2020

IJETER

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Ensemble classifiers are widely used for the enhancement of accuracy of twitter sentiment classification. In the present research, a hybrid model based on stack based ensemble classifiers and dictionary based classifier is used for tweet classification as positive and negative. To enhance accuracy of classification, sentiment score retrieved from dictionary based classifier is added to the feature vector to get enhanced feature set and the hybrid stack based ensemble model is implemented on this enhanced feature set. Three machine learning classifiers svmRadial, C5.0, NB are used to build stacked based ensemble classifier using GLM and RF as Meta learners. Three data sets viz. Kaggle -US Airline Twitter Sentiment Data Set, Sentiment 140 Twitter Data Set, and Real time manually labeled data set related to 'Clean India Mission' are used for the implementation of the proposed model. Caret library of R Studio is used for creating the stack based ensemble of classifiers. The results show that the proposed hybrid model that used sentiment score as one of the features in feature set performed better with an accuracy of 0.8742223 for Kaggle -US Airline Twitter Sentiment Data Set, 0.8881453 for data set related to 'Clean India Mission' and 0.9953593 for Sentiment 140 Twitter Data Set, as compared to machine learning classifiers and other ensemble classifiers.

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Sentiment Analysis on User Satisfaction Level of Mobile Data Services Using Support Vector Machine (SVM) Algorithm

Cited by 14 publications

References 10 publications

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Analyzing impact of number of features on efficiency of hybrid model of lexicon and stack based ensemble classifier for twitter sentiment analysis using WEKA tool

Hybrid Model using Stack-Based Ensemble Classifier and Dictionary Classifier to Improve Classification Accuracy of Twitter Sentiment Analysis

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