[Air-L] Text Data in Marketing: Data Sources, Linguistic Features, and Software Programs

[Thomas Ball]

See this article...

Netzer, Oded, Ronen Feldman, Jacob Goldenberg and Moshe Fresko (2012), *"Mine
Your Own Business: Market Structure Surveillance through Text
Mining."* *Marketing
Science*, 31 (3), 521-543

On Wed, Oct 9, 2019 at 9:26 AM Astvansh, Vivek <astvansh at iu.edu> wrote:

> Dear Internet Researchers:
>
> I am delivering a demo to my department faculty, titled: Text Data in
> Marketing: Data Sources, Linguistic Features, and Software Programs. I
> request you to critique my coverage and suggest changes/additions.
>
> As the title indicates, I will cover the following three aspects:
>
>
> 1.      Show, via an example, text data in marketing:
>
> a.       firm-generated (e.g., earnings calls)
>
> b.      consumer/user-generated text data (e.g., Twitter)
>
> c.       other-generated, marketing-relevant text data. Other could
> include market stakeholders (competitors, suppliers, organizational
> customers) and nonmarket stakeholders (news media, consumer organizations,
> regulators, legislators)
>
> I will take the example of the Volkswagen emissions scandal and show how
> this event led to text data generated by Volkswagen and its varied
> marketing stakeholders. I will mention various secondary data sources that
> my colleagues can use to obtain/buy text data.
>
> If you know of any other source, or an example more insightful than
> Volkswagen, please help me.
>
>
> 2.      I will then proceed to discuss linguistic features of the text.
> These features include sentiment, emotion, cognition, named entities,
> readability, subjectivity, structural complexity, lexical complexity, and
> topic modeling/mining. I choose these features because I have used them in
> my research and can talk about them.
>
> If you know of any other useful feature that I am missing, please respond.
>
>
> 3.      Software programs, both paid (e.g., LIWC) and free R/Python
> libraries, that take text as input and output the above linguistic
> features. I will start with LIWC, explain its variables from psychological
> standpoint. I will then demo syuzhet and sentimentr showing how their
> output variables offer new and different insights relative to LIWC. I will
> mention other R and Py packages such as TensorFlow, MXNet, and TextBlob).
>
> I will demo MALLET GUI for topic modeling. I will then mention how
> researchers can use MTurk to annotate their text data and then write a
> classifier (or hire a Py programmer to write it for them).
>
> If you know of any paid software program (that is as easy to use as LIWC)
> or any other R/Py package, please suggest.
>
> Thank you!
> Vivek Astvansh
> Assistant Professor of Marketing,
> Kelley School of Business, Indiana University
> http://kelleyschool.iu.edu/astvansh  | +1 (812) 855-8953
>
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