Twitter’s Sentiment Analysis on Gsm Services using Multinomial Naïve Bayes

Telecommunication users are rapidly growing each year. As people keep demanding a better service level of Short Message Service (SMS), telephone or data use, service providers compete to attract their customer, while customer feedbacks in some platforms, for example Twitter, are their souce of information. Multinomial Naïve Bayes Tree, adapted from the method of Multinomial Naïve Bayes and Decision Tree, is one technique in data mining used to classify the raw data or feedback from customers.Multinomial Naïve Bayes method used specifically addressing frequency in the text of the sentence or document. Documents used in this study are comments of Twitter users on the GSM telecommunications provider in Indonesia.This research employed Multinomial Naïve Bayes Tree classification technique to categorize customers sentiment opinion towards telecommunication providers in Indonesia. Sentiment analysis only included the class of positive, negative and neutral. This research generated a Decision Tree roots in the feature "aktif" in which the probability of the feature "aktif" was from positive class in Multinomial Naive Bayes method. The evaluation showed that the highest accuracy of classification using Multinomial Naïve Bayes Tree (MNBTree) method was 16.26% using 145 features. Moreover, the Multinomial Naïve Bayes (MNB) yielded the highest accuracy of 73,15% by using all dataset of 1665 features. The expected benefits in this research are that the Indonesian telecommunications provider can evaluate the performance and services to reach customer satisfaction of various needs

Research-Based on Telecommunication in Mobile Service Provider's Performance using Enhanced Naive Bayes Classifier

In recent years, mobile service providers have rapidly expanded across all countries. Considering unpredictable development trends, mobile service providers are essential to knowledge-based service businesses. Performance may be improved by creating and disseminating new information through innovation activities based on the usage of business intelligence. This research examined the performance of mobile service providers across all countries utilizing an enhanced Naive Bayes classifier based on telecommunication. In comparison to quantitative variables, the naive Bayes performs quite well. In the beginning, data is collected and the normalization technique is used for data preprocessing. Feature extraction is carried out using “Term Frequency and Inverse Document Frequency (TF-IDF)”. “Decision Tree algorithm” is used for data analysis. Then the feature is selected using a two-stage Markov blanket algorithm. Enhanced Naïve Bayes Classifier is the proposed algorithm for telecommunication analysis and at last, the performance of the system is analyzed. This proposed algorithm is used to compare the mobile service provider's performances with existing algorithms. The proposed method measures the following metrics as Throughput, Packet loss, Packet duplication, and User quality of experience. The proposed algorithm is more effective and produces better results.&nbsp

Predictive analytics of Churn Customers Calling Details Records using Classification by Clustering (CBC) dealing with Supervised Machine Learning Algorithms

Telecom companies generate enormous amounts of data regularly. The telecom Decision makers that obtaining new customers is more challenging than sustaining existing ones. Furthermore, data from existing churn customers may be utilized to detect churn clients and their patterns of behavior. This research develops a model of churn prediction for the telecommunication business, which uses NB, SVM, DT, and RDF to detect churn clients. The proposed model churns customers' data using classification techniques, with the Random Forest (RDF) method performing well (95.94 % correctly categorized instances), the Decision Tree (DTs) providing classification accuracy (95.40 %), the Naïve Bayes (NB) provided classification accuracy (89.58 %), and the Support Vector Machine (SVMs) provided classification accuracy (71.08 %). The four different classification algorithms' predictions and observations are compared, with a percentage of 71 percent equality and 29 percent variation

Customer sentiment analysis through social media feedback: A case study on telecommunication company

Customer sentiment analysis is an automated way of detecting sentiments in online interactions in order to assess customer opinions about a product, brand or service. It assists companies in gaining insights and efficiently responding to their customers. This study presents a machine learning approach to analyse how sentiment analysis detects positive and negative feedback about a telecommunication company’s products. Customer feedback data were taken from Twitter through Streaming API (Application Programming Interface), where Tweets are retrieved in real time based on search terms, time, users and likes. Responses from the twitter API are parsed into tables and stored in a CSV file. Based on the analysis, it was found that there was no negative sentiment from the customers. The data were then split into training and testing to be tested on the three different supervised learning algorithms used in this study which are Support Vector Machine, Random Forest, and Naïve Bayes. Lasty, the performance of each model was compared to select the most accurate model and from the analysis, it can be concluded that Support Vector Machine gives the best performance in terms of accuracy, Mean Squared Error, Root Mean Squared Error and Area Under the ROC curve

Twitter Analysis to Predict the Satisfaction of Saudi Telecommunication Companies’ Customers

The flexibility in mobile communications allows customers to quickly switch from one service provider to another, making customer churn one of the most critical challenges for the data and voice telecommunication service industry. In 2019, the percentage of post-paid telecommunication customers in Saudi Arabia decreased; this represents a great deal of customer dissatisfaction and subsequent corporate fiscal losses. Many studies correlate customer satisfaction with customer churn. The 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. Current methods of analysing churn rates are inadequate and faced some issues, particularly in the Saudi market. This research was conducted to realize the relationship between customer satisfaction and customer churn and how to use social media mining to measure customer satisfaction and predict customer churn. This research conducted a systematic review to address the churn prediction models problems and their relation to Arabic Sentiment Analysis. The findings show that the current churn models lack integrating structural data frameworks with real-time analytics to target customers in real-time. In addition, the findings show that the specific issues in the existing churn prediction models in Saudi Arabia relate to the Arabic language itself, its complexity, and lack of resources. As a result, I have constructed the first gold standard corpus of Saudi tweets related to telecom companies, comprising 20,000 manually annotated tweets. It has been generated as a dialect sentiment lexicon extracted from a larger Twitter dataset collected by me to capture text characteristics in social media. I developed a new ASA prediction model for telecommunication that fills the detected gaps in the ASA literature and fits the telecommunication field. The proposed model proved its effectiveness for Arabic sentiment analysis and churn prediction. This is the first work using Twitter mining to predict potential customer loss (churn) in Saudi telecom companies, which has not been attempted before. Different fields, such as education, have different features, making applying the proposed model is interesting because it based on text-mining

Mapping consumer sentiment toward wireless services using geospatial twitter data

Hyper-dense wireless network deployment is one of the popular solutions to meeting high capacity requirement for 5G delivery. However, current operator understanding of consumer satisfaction comes from call centers and base station quality-of-service (QoS) reports with poor geographic accuracy. The dramatic increase in geo-tagged social media posts adds a new potential to understand consumer satisfaction towards target-specific quality-of-experience (QoE) topics. In our paper, we focus on evaluating users’ opinion on wireless service-related topics by applying natural language processing (NLP) to geo-tagged Twitter data. Current generalized sentiment detection methods with generalized NLP corpora are not topic specific. Here, we develop a novel wireless service topic-specific sentiment framework, yielding higher targeting accuracy than generalized NLP frameworks. To do so, we first annotate a new sentiment corpus called SignalSentiWord (SSW) and compare its performance with two other popular corpus libraries, AFINN and SentiWordNet. We then apply three established machine learning methods, namely: Naïve Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network (RNN) to build our topic-specific sentiment classifier. Furthermore, we discuss the capability of SSW to filter noisy and high-frequency irrelevant words to improve the performance of machine learning algorithms. Finally, the real-world testing results show that our proposed SSW improves the performance of NLP significantly

Efficient and Trustworthy Review/Opinion Spam Detection

The most common mode for consumers to express their level of satisfaction with their purchases is through online ratings, which we can refer as Online Review System. Network analysis has recently gained a lot of attention because of the arrival and the increasing attractiveness of social sites, such as blogs, social networking applications, micro blogging, or customer review sites. The reviews are used by potential customers to find opinions of existing users before purchasing the products. Online review systems plays an important part in affecting consumers' actions and decision making, and therefore attracting many spammers to insert fake feedback or reviews in order to manipulate review content and ratings. Malicious users misuse the review website and post untrustworthy, low quality, or sometimes fake opinions, which are referred as Spam Reviews. In this study, we aim at providing an efficient method to identify spam reviews and to filter out the spam content with the dataset of gsmarena.com. Experiments on the dataset collected from gsmarena.com show that the proposed system achieves higher accuracy than the standard na?ve bayes

PENERAPAN NAIVE BAYES CLASSIFIER DALAM KLASIFIKASI SENTIMEN KOMENTAR TERHADAP VIDEO TRAILER FILM PADA FACEBOOK FANSPAGE

Film merupakan subjek yang diminati oleh sejumlah besar orang diantara komunitas jaringan sosial yang memiliki perbedaan signifikan dalam pendapat atau sentimen mereka. Salah satu cara untuk mempromosikan film yaitu melalui video trailer yang disebarkan di sosial media seperti Facebook. Trailer sendiri merupakan sebuah ekstrak dari sebuah film ataupun siaran yang dipublikasikan. Melalui video trailer yang disebarkan di sosial media para penikmat film dapat berkritik maupun berkomentar pada film yang akan ditayangkan, baik itu komentar positif dan negatif. tentunya data komentar ini dapat dijadikan sumber data dalam penelitian text mining untuk klasifikasi jenis komentar. Penelitian ini memanfaatkan hal tersebut dengan menggunakan salah satu metode klasifikasi Naïve Bayes Classifier (NBC). Data komentar diperoleh dari fanspage facebook, peneltian ini menggunakan dataset berjumlah 1000 data yang akan diklasifikasikan kedalam kelas positif dan negatif. Hasil pengujian mendapatkan akurasi tertinggi pada rasio pengujian 90 : 10% dengan nilai akurasi yang cukup baik yaitu 81%, nilai precision 65%, dan nilai recall 62%. Berdasarkan hasil penelitian yang dilakukan dapat disimpulkan bahwa Naive Bayes Classifier berhasil mengklasifikasikan komentar dengan sentimen yang bersifat positif dan negatif. Kata Kunci : Facebook, Klasifikasi, Naïve Bayes Classifier, Sentimen, Traile

Toward Understanding Personalities Working on Computer: A Preliminary Study Focusing on Collusion/Plagiarism

Ample research has been carried out in the area of collusion, plagiarism and e-learning. Collusion is a form of active cheating where two or more parties secretly or illegally corporate. Collusion is at the root of common knowledge plagiarism. While plagiarism requires two or more entities to compare, collusion can be determined in isolation. It is also possible that collusion do not lead to positive plagiarism checks. It is therefore the aims of this preliminary study to: (i) identify the factors responsible for collusion in e-learning (ii) determine the prominent factor that is representative of collusion and (iii) through user behaviour including, but not limited to, application switching time, determine collusion. We claim that user computer activities and application processes can help understand user behaviour during assessment task. It is on this premise that we develop a machine learning model to predict collusion through user behaviour during assessment tas

Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media

With the advancement of social media networks, there are lots of unlabeled reviews available online, therefore it is necessarily to develop automatic tools to classify these types of reviews. To utilize these reviews for user perception, there is a need for automated tools that can process online user data. In this paper, a sentiment analysis framework has been proposed to identify people’s perception towards mobile networks. The proposed framework consists of three basic steps: preprocessing, feature selection, and applying different machine learning algorithms. The performance of the framework has taken into account different feature combinations. The simulation results show that the best performance is by integrating unigram, bigram, and trigram features