Telstra's Social Media Mining Tools: Strategy and Management Report

This report examines the social media mining tools employed by Telstra, an Australian telecommunications and media company, to analyze social networking data, particularly Twitter feeds. The analysis focuses on various tools used for data mining, processing, and modeling, including R, Mathematica, MATLAB, and business toolkits like SAS Text Analytics. The report also covers social network monitoring instruments such as Amplified Analytics and Social Mention, and text analysis tools like Stanford NLP and Python NLTK. It highlights the use of data visualization software like Tableau and SAS Visual Analytics for business intelligence. The conclusion emphasizes the importance of these tools for staying updated with marketing strategies and enhancing community engagement. The report provides an overview of the tools and their applications within the context of Telstra's operations, along with references to relevant research.

Running head: SOCIAL MEDIA MINING TOOLS 1
Social media strategy and management in Telstra
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Telstra Overview
This report will be based on Telstra corporation limited which is an Australian
telecommunication and media firm. Famously known Telstra, the company builds and operates
telecom networks, internet access, entertainments, television shows, and market voice.
Analyzing social networking, Twitter feeds, in particular, for valuation examination, has become
an important research and business movement since the ease of access to electronic application
program designs interfaces given by Facebook, Twitter, and News managements, and this is key
to Telstra. Telstra has impelled a blast of data management and programming devices for
scraping and investigation, as well as, social media evaluation stages. This paper explores
various social media mining tools utilized today by Telstra.
Social media mining tools
Data mining from social media can be quite tasking due to the numerous number of social
networking platforms available. Scientific examination tools have been upgraded to offer help
for sourcing, seeking and investigating content. Illustrations include applicable at Telstra in the
utilization for numeric logical programming are R used for factual programming, and
Mathematica utilized for PC polynomial math, representative logical programming (Aggarwal, &
Zhai, 2012).
Information preparing and information displaying, for example, relapse examination which gives
time-arrangement investigation, GUI and exhibit based measurements, are direct utilizing
MATLAB. MATLAB is fundamentally quicker than the customary programming dialects and
can be utilized for an extensive variety of utilizations in Telstra. Also, the thorough implicit
plotting capacities make it a complex investigation toolbox. All the more computationally

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capable calculations can be created utilizing it in conjunction with the bundles (Gundecha, &
Liu, 2012).
Business toolkits are tools that enable clients to source, look and dissect content for a variety of
business motives as offered by Telstra. SAS Text Analytics program through the SAS Sentiment
Analysis Manager can be utilized for scraping data sources, including standard Web destinations
and online networking outlets, and also inner hierarchical content sources, and makes reports that
depict the communicated sentiments of shoppers, clients and rivals continuously (Zafarani,
Abbasi, & Liu, 2014).
Social network monitoring instruments are sentiment investigation devices for following and
measuring what individuals are stating about an organization or its items, or any point over the
web-based social networking scene. Examples include: Amplified Analytics which concentrates
on item audits and promoting information; Social Mention, which gives online networking alerts
comparatively to Google Alerts (Batrinca, & Treleaven, 2015). Different illustrations involve:
Trackur, which is a social media notoriety checking apparatus that monitors what people post the
Internet and Lithium Social Network Monitoring.
Text examination tools are expansive based devices for normal dialect handling and content
investigation. Cases of organizations in the content examination region include: Telstra whose
devices consequently channel and total contemplations, emotions and explanations from
conventional and social media. There are additionally a wide range of unreservedly accessible
tools created by non-government associations and academic groups for sourcing, seeking and
analyzing conclusions. Examples that incorporate the examination of human language are
Stanford NLP assemble devices and LingPipe. An assortment of open-source content analysis
tools are accessible, particularly for estimation examination (Barbier, & Liu, 2011). A well-

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known content examination device is Python NLTK an ordinary Linguistic toolkit. It is also an
open source.
The information visualization software provides commercial intelligence abilities and enable
diverse sorts of clients to obtain perspectives from Big Data at Telstra. The operators perform
empirical investigation through intuitive user-interface accessible on the dominant part of
gadgets, with a current focus on smart phones and tablets (Safko, 2010). The information
perception devices enable the users to distinguish examples, patterns and connections in the
information which were beforehand inactive. The two most outstanding imagining tools are
Tableau and SAS Visual Analytics and are highly adopted at Telstra.
Conclusion
The greatest concern is that organizations are progressively limiting the access to their
information to commercialize their content. Most of these tools give a real-time pursuit
component to make data from social discussions more viable. They enable saving time and
money by making it simple to screen an organization's image's execution at the business
environment. Incorporating these instruments into the marketing blend would ensure that the
marketing strategy to remains updated and enhances engagement with the local community.

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References
Aggarwal, C. C., & Zhai, C. (Eds.). (2012). Mining text data. Springer Science & Business
Media.
Barbier, G., & Liu, H. (2011). Data mining in social media. Social network data analytics, 327-
352.
Batrinca, B., & Treleaven, P. C. (2015). Social media analytics: a survey of techniques, tools and
platforms. AI & SOCIETY, 30(1), 89-116.
Gundecha, P., & Liu, H. (2012). Mining social media: a brief introduction. In New Directions in
Informatics, Optimization, Logistics, and Production (pp. 1-17). Informs.
Safko, L. (2010). The social media bible: tactics, tools, and strategies for business success. John
Wiley & Sons.
Zafarani, R., Abbasi, M. A., & Liu, H. (2014). Social media mining: an introduction. Cambridge
University Press.