AT&T vs Verizon: Mining Twitter for Customer Satisfaction towards North American Mobile Operators

The North American Telecommunications sector is one of the leading mobile broadband sectors worldwide, representing increasingly important revenue opportunities for mobile operators. Taking into consideration that the market is being saturated and revenue from new subscriptions is increasingly deteriorating, mobile carriers tend to focus on customer service and high levels of customer satisfaction in order to retain customers and maintain a low churn rate. In this context, it is a matter of critical importance to be able to measure the overall customer satisfaction level, by explicitly or implicitly mining the public opinion towards this end. In this paper, we argue that online social media can be exploited as a proxy to infer customer satisfaction through the utilization of automated, machine-learning based sentiment analysis techniques. Our work focuses on the two leading mobile broadband carriers located in the broader North American area, AT&T and Verizon, by analysing tweets fetched during a 15-day period within February 2013, to assess relative customer satisfaction degrees. The validity of our approach is justified through comparison against surveys conducted during 2012 from Forrester and Vocalabs in terms of customer satisfaction on the overall brand - usage experience

Social Network Analysis Within The ICMB Community: Co-Authorship Networks

Founded in Athens during 2002, ICMB developed to the major international research conference on mobile business with a significant number of researchers and authors contributing state of the art scientific papers in academia. In this paper we examine the state of the ICMB co-authorship network from 2002 to 2013 by applying Social Network Analysis techniques and measures. Our analysis is based on a network model generated by data gathered from papers featured in the aforementioned conferences. Our analysis consists of metrics such as clustering analysis, degree, betweenness centrality measures as well as network component related properties. These measures aim to answer a wide range of questions about collaboration patterns, such as the numbers of papers submitted, coauthorships, and showcase how patterns of collaboration emerge between larger scale, tightly connected node formations of the co-authorship network

Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices

This paper uses time-series analysis to study the relationship between Bitcoin prices and fundamental economic variables, technological factors and measurements of collective mood derived from Twitter feeds. Sentiment analysis has been performed on a daily basis through the utilization of a state-of-the- art machine learning algorithm, namely Support Vector Machines (SVMs). A series of short-run regressions shows that the Twitter sentiment ratio is positively correlated with Bitcoin prices. The short-run analysis also reveals that the number of Wikipedia search queries (showing the degree of public interest in Bitcoins) and the hash rate (measuring the mining difficulty) have a positive effect on the price of Bitcoins. On the contrary, the value of Bitcoins is negatively affected by the exchange rate between the USD and the euro (which represents the general level of prices). A vector error- correction model is used to investigate the existence of long-term relationships between cointegrated variables. This kind of long-run analysis reveals that the Bitcoin price is positively associated with the number of Bitcoins in circulation (representing the total stock of money supply) and negatively associated with the Standard and Poor’s 500 stock market index (which indicates the general state of the global economy)