Model-based sentiment analysis of customer satisfaction for the Jordanian telecommunication companies

Najadat, Hassan; Al-Abdi, Amnah; Sayaheen, Yasmeen

doi:10.1109/iacs.2018.8355429

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…Another study, performed by Najadat et al in 2018, sentiment analysis of customer status on the official Facebook pages of 3 Jordanian telecommunications companies using and comparing several supervised learning methods, which are K Nearest Neighbors, Support Vector Machine, Naïve Bayes, and Decision Tree. The results obtained are that SVM is the most superior compared to the other three methods in terms of accuracy and Fmeasure in each experimental scenario [7].…”

Section: Introductionmentioning

confidence: 93%

Sentiment and Discussion Topic Analysis on Social Media Group using Support Vector Machine

Adityani

Richasdy

Astuti

2022

mib

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The growth of social media in this modern era is increasingly rapid, where people are very active digitally interacting with each other. People who have a common interest or simply like to be in a community often gather in an online group, especially on Facebook. Alumni of Telkom University are no exception, who are also actively discussing and sharing information in Telkom University Alumni Forum Facebook group (FAST). By using their status from that group, sentiment and topic discussion analysis can be performed to determine whether the polarity is positive, neutral, or negative. In Addition, topic modeling extracts what topics are often discussed in the group. In this research, sentiment analysis was performed using the Support Vector Machine (SVM) method. Also, the classification process involved TF-IDF for word weighting and confusion matrix as performance measurement. Several testing scenarios were carried out to get the best accuracy value. Based on the tests performed on the preprocessing technique and feature extraction n-gram addition, the highest accuracy value obtained is 80.56%. The result indicates that the best performance is obtained by combining preprocessing techniques without the stopword removal process and feature extraction unigram. Moreover, the topics discussed based on topic modeling results were related to telecommunication and Telkom, Indonesia, alumni, and FAST.

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Section: Introductionmentioning

confidence: 93%

Sentiment and Discussion Topic Analysis on Social Media Group using Support Vector Machine

Adityani

Richasdy

Astuti

2022

mib

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“…Furthermore, [32] employed Twitter as a medium for conducting sentiment analysis by scrutinising tweets in the English language originating from diverse businesses in Saudi Arabia. The researchers used K-nearest neighbour and naive Bayes algorithms to classify attitudes into three categories: positive, negative, and neutral.…”

Section: Background Of Studymentioning

confidence: 99%

Evaluating User Satisfaction Using Deep-Learning-Based Sentiment Analysis for Social Media Data in Saudi Arabia’s Telecommunication Sector

Alshamari

2023

Computers

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Social media has become common as a means to convey opinions and express the extent of satisfaction and dissatisfaction with a service or product. In the Kingdom of Saudi Arabia specifically, most social media users share positive and negative opinions about a service or product, especially regarding communication services, which is one of the most important services for citizens who use it to communicate with the world. This research aimed to analyse and measure user satisfaction with the services provided by the Saudi Telecom Company (STC), Mobily, and Zain. This type of sentiment analysis is an important measure and is used to make important business decisions to succeed in increasing customer loyalty and satisfaction. In this study, the authors developed advanced methods based on deep learning (DL) to analyse and reveal the percentage of customer satisfaction using the publicly available dataset AraCust. Several DL models have been utilised in this study, including long short-term memory (LSTM), gated recurrent unit (GRU), and BiLSTM, on the AraCust dataset. The LSTM model achieved the highest performance in text classification, demonstrating a 98.04% training accuracy and a 97.03% test score. The study addressed the biggest challenge that telecommunications companies face: that the company’s services influence customers’ decisions due to their dissatisfaction with the provided services.

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“…The purpose of this is the need of marketing intelligence for decision making about the market and its competition. Other than Twitter, a researcher from [27] used the Facebook pages of different Jordanian telecommunication brands to analyze sentiments that are in a Jordanian dialect in customer posts. All the sentiments that are gathered and analyzed are classified manually using four main classifiers: support vector machine (SVM), K-nearest neighbor, naive Bayes, and decision tree.…”

Section: Analyze Failure Trends and Classify Customer Feedbacksmentioning

confidence: 99%

Innovative Business Process Reengineering Adoption: Framework of Big Data Sentiment, Improving Customers’ Service Level Agreement

Susanto

Sari

Leu

2022

BDCC

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Social media is now regarded as the most valuable source of data for trend analysis and innovative business process reengineering preferences. Data made accessible through social media can be utilized for a variety of purposes, such as by an entrepreneur who wants to learn more about the market they intend to enter and uncover their consumers’ requirements before launching their new products or services. Sentiment analysis and text mining of telecommunication businesses via social media posts and comments are the subject of this study. A proposed framework will be utilized as a guideline, and it will be tested for sentiment analysis. Lexicon-based sentiment categorization is used as a model training dataset for a supervised machine learning support vector machine. The result is very promising. The accuracy and the quantity of the true sentiments it can detect are compared. This result signifies the usefulness of text mining and sentiment analysis on social media data, while the use of machine learning classifiers for predicting sentiment orientation provides a useful tool for operations and marketing departments. The availability of large amounts of data in this digitally active society is advantageous for sectors such as the telecommunication industry. These companies can be two steps ahead with their strategy and develop a more cohesive company that can make customers happier and mitigate problems easily with the use of text mining and sentiment analysis for further adopting innovative business process reengineering for service improvements within the telecommunications industry.

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Model-based sentiment analysis of customer satisfaction for the Jordanian telecommunication companies

Cited by 8 publications

References 6 publications

Sentiment and Discussion Topic Analysis on Social Media Group using Support Vector Machine

Sentiment and Discussion Topic Analysis on Social Media Group using Support Vector Machine

Evaluating User Satisfaction Using Deep-Learning-Based Sentiment Analysis for Social Media Data in Saudi Arabia’s Telecommunication Sector

Innovative Business Process Reengineering Adoption: Framework of Big Data Sentiment, Improving Customers’ Service Level Agreement

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