Twitter opinion mining predicts broadband internet's customer churn rate

Napitu, Fiernad; Bijaksana, Moch Arif; Trisetyarso, Agung; Heryadi, Yaya

doi:10.1109/cyberneticscom.2017.8311699

Cited by 14 publications

(10 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the following, these related research in Table 1 are analysed in more details. The only other study to use of tweet sentiment analysis for business [28] found that there is a relationship between the sentiment of tweet feeds related to Telcom's broadband internet service and the customer churn rate. They applied the long short-term memory model (LSTM) for sentiment analysis.…”

Section: Predicting Customer Churn and Data Mining Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

2021

View full text Add to dashboard Cite

With the rising growth of the telecommunication industry, the customer churn problem has grown in significance as well. One of the most critical challenges in the data and voice telecommunication service industry is retaining customers, thus reducing customer churn by increasing customer satisfaction. Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. The related research reveals that many studies have focused on developing churner prediction models based on historical data. These models face delay issues and lack timelines for targeting customers in real-time. In addition, these models lack the ability to tap into Arabic language social media for real-time analysis. As a result, the design of a customer churn model based on real-time analytics is needed. Therefore, this study offers a new approach to using social media mining to predict customer churn in the telecommunication field. This represents the first work using Arabic Twitter mining to predict churn in Saudi Telecom companies. The newly proposed method proved its efficiency based on various standard metrics and based on a comparison with the ground-truth actual outcomes provided by a telecom company.

show abstract

Section: Predicting Customer Churn and Data Mining Techniquesmentioning

confidence: 99%

“…Earlier studies differed in setting the time window for churning analysis and prediction. For instance, reference [28] proved that a customer mood in Twitter could be a predictor for churning three months later. In addition, reference [48] collected the threemonth call data of customers from a Jordanian telecommunication company.…”

Section: Data Set Constructionmentioning

confidence: 99%

An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

2021

View full text Add to dashboard Cite

show abstract

“…The CNN algorithm -a variant of ANN-is made up of neurons that have learnable weights and biases, where each neuron receives an input, performs a dot product and optionally follows it with non-linearity. In total, 12 studies [286,293,288,232,290,294,90,158,57,295,166,296] made use of this algorithm. Notably, the authors in [158] propose a language-agnostic translation-free method for Twitter sentiment analysis.…”

Section: Algorithm Number Of Studies Referencementioning

confidence: 99%

“…• Telecommunications (e.g., telephony, television) on particular service providers [104,120,294,485,161,77,186,514,465] or complaints [499];…”

Section: Application Areas Of Social Opinion Miningmentioning

confidence: 99%

Over a Decade of Social Opinion Mining: A Systematic Review

Cortis,

Davis

2020

Preprint

View full text Add to dashboard Cite

Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing and other

show abstract

“…Fiernad Napitu [1] has proposed that Churn rate analysis has numerous techniques that make use of the following customer activities such as payment behavior, usage, complaint data and tenure. This paper deals with the churn rate prediction with the help of churn analysis techniques which are in need to acknowledge a number of other important factors.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Opinion Behavior using Content based Sequential Opinion Influence Model

Elangovan¹

2019

IJRASET

View full text Add to dashboard Cite

Micro-blogging and social networking sites play a vital role in identifying and extracting distinct opinions and emotions from public. Social media has become a viral medium for public to express and exchange their opinions through the user-generated text. The aim of this paper is to classify the tweets using Content based algorithm and Support Vector Machine (SVM). The tweets are also analyzed and classified in terms of polarity: Positive, Negative and Neutral. To keep track of user opinion behaviors and infer user opinion influence from the historical exchanged textual information, we develop a contentbased sequential opinion influence framework with the help of two opinion sentiment prediction models with alternative prediction strategies are proposed.

show abstract

Twitter opinion mining predicts broadband internet's customer churn rate

Cited by 14 publications

References 12 publications

An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

Over a Decade of Social Opinion Mining: A Systematic Review

Prediction of Opinion Behavior using Content based Sequential Opinion Influence Model

Contact Info

Product

Resources

About