Tracking by modify the services

Tracking how a change in a service affects Telecom Customers feeling using Sentiment Analysis ‘Naïve Bayes’Amar Mustafa{{}}Abdelsadek{{}}AbstractTracking the effect of change a service on the telecom customer feeling is very important analysis for Telecom Companies.

As a result of fast growth and severe competition, customer retention and managing high churn rate are the most important challenges faced by telecom companies today. Customer retention can be achieved by identifying the feeling of the telecom customers after changing a service and take care about telecom customers by modify the services that reach low score of customer willing. This paper was done by using a combination among four stages of text preprocessing, personality analysis, and sentiment analysis and chat bot system is created to achieve the needed task. This paper show the effect of using the personality traits (agreeableness, emotional range) with sentiment analysis that help for reaching to a full description about customer feeling. The proposed solution achieved accuracy of 95% of determining the customer feeling. Combining the Sentiment Analysis ‘Naïve Bayes technique’ in the natural language processing and personality insights pre learning stage and adding a feedback using the obtained results achieve higher accuracy than using the traditional sentiment analysis techniques.Introduction With the growth of telecom companies such as Etisalat ,Orange and Vodafone in our country, this are cause of increasing the telecom customer data.

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Computational linguists have taken advantage of these data, mostly addressing prediction tasks such as sentiment analysis, personality analysis and emotion detection. A few works have also been devoted to predicting what the customer feeling about the new service. Prediction tasks have many useful applications ranging from tracking opinions about service to identifying the best and bad service and predicting of the telecom customer satisfaction and so on. What we need: – Telecom companies get a bad rap when it comes to customer experience.

All too often, clients feel that service falls short of their expectations, and that complaints seem to be falling on deaf ears. Yet despite poor customer sentiment, few telecom companies have made customer centricity a priority. So in this paper we need to show how the using of customer textual data that come from some tools such as AI chat bot help on determine if the telecom customers are willing or not with the new service, if not, how we can help them?, what is the percent of success and accept the new service. What are the services that a telecom customer does not need? So we can help the telecom companies to live day per day with its customers from the behavioral side.

If you know aspects and themes in each response, you can also answer questions like: For how long do people react negatively to a change in a service, or do they really love the new feature added? In nowadays we observe the raise of telecom companies so, how can we help telecom companies to prevent its customer from migrate to another telecom company because of the bad of service?By analyzing the sentiment more accurately, and in particular finding the services that telecom customer are really unhappy about, you can:- Focus more on what will make a difference.- Help users to find what they needs; help increasing telecom customer’s satisfaction.- Help Telecom Company to produce the best service for its customers.- Make the company work easier, Help Company to keep of their customers.- Help Telecom Company for improve its customer care by keep track what the percent of acceptance that the new service generates.- If you know aspects and themes in each response, you can also answer questions like: For how long do people react negatively to a change in a service, or do they really love the new feature added?With this in mind that the symptoms of this problem are very dangerous ones, telecom companies ignoring rate for the symptoms leads to migration of its telecom customers.

So detecting the problem automatically from telecom customer conversation using sentiment analysis and personal insights is a vital process to give early warnings before it gets dangerous. Help Telecom Company to understand its weakness point for re-correcting it, help all kind of user to contact with the telecom company by developing the slang language chat bot. preventing telecom customers from leaving the telecom company to another one.The problem entities are how understand and use the agreeableness and emotional range personality traits and its sub traits, the sentiment analysis, emotional values of the telecom customers as showing in figure1, and how we use this traits for reaching to detailed report about customer feeling. Figure 1: showing the cooperation between customer traits (agreeableness and Emotional range) and sentiment analysis in tracking telecom customer feeling.

conversation data after changing a service to determine the success of the service and willing of the customers about it, how the success of change in a service can see in customer conversation text as we know that “The pen is mightier than the sword”, So what we are trying to do is to automatically recognize the feeling of the telecom customer using a mix of personality analysis and some sort of ML sentiment analysis, NLP and textual AI algorithms, figure2 show this processes. Doing the automatic recognition of the telecom customer sentiment is expected to increase the efficiency to be higher than 95%.The assumption behind this methodology is that Textual data especially those expressing concerns, frustrations and acceptance from customers are rich in knowledge which needs to be mined for insights. Passed on (Pang and Lee, 2008) Sentiment analysis is based on categorizations of particular words as ‘positive’ or negative. Algorithms based on presenting conversations in response to such emotional words have to be ‘trained’ on this data. For sentiment analysis in particular, there are many issues with training data, because the procedure depends on the assumption that words are most often associated with particular feelings.

Sentiment analysis algorithms can have difficulty identifying when a word is used sarcastically, for example ,Sentiment analysis, unlike classical text mining which focuses on topical words, picks only sentiment signals for real time analysis. On the other hand and according to (Brent W. Roberts and Daniel Mroczek, 2008), a common assumption is that personality traits act like metabolic set points.

People may stray briefly from their biological propensity, but they will then tend to drift back to their genetically driven set point. Under these types of models, one would expect to find a negative or null association between time and mean-level change, because any change will represent short-term fluctuations that disappear as people return to their need point so in this work we try to use the customer personality insights and compute the agreeableness and emotional Range traits that help in tracking customer feeling. The proposed system is built over a state of the art machine learning algorithm used for learning process of sentiment analysis. It is developed by combining these four factors: (1) text preprocessing, (2) sentiment analysis, (3) personal analysis and (4) reporting stage and a chat bot system is created to achieve the needed task as showing in figure 2.

the textural data of telecom customer conversations on chat bot, pass through set of NLP algorithms, for doing the need data preprocessing, computing bag of words and set of text preprocessing, then we can passing it to the pre trained naïve bays classifier for identify the customer sentiment, on the other hand we use the IBM personality insights (PI) API for computing the customer big five traits, feeding the two result to a pre trained ML algorithm will increase the accuracy to reach to values higher than 95%.we will discuss this in detail, then we can use all of this results for helping telecom customers get what they need.