Session 5 Homework

Update:

Sorry folks, typed in the wrong URL by mistake. Those looking for sessions 8 and 9 pre-reads, pls go here.

The following are pre-reads for session 7:

1. AI meets the C-Suite (McKinsey Quarterly)

and 2. Track Customer Attitudes to predict their behaviors (HBR)

The course-pack reading for session 7 is optional and these two are mandatory.

Sudhir

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Hi all,

You might want to lookup the list of shiny apps available listed in the session 5 updates blog post here, before we start.

The homework has three parts. Only two of the three need be done and submitted.

Homework part 1: (group submission, mandatory)

Choose any non-obscure product or service on Flipkart or Amazon (or any other review aggregation source).

Your R.O.s are: (1) Find the top few things people like about the product.

(2) Find the top few things people dislike about the product.

(3) Suggest a (re-)positioning strategy for the product based on the above.

Pull 100+ reviews of the product.

Note: A Flipkart shinyapp is available already. Just follow instructions on the first page of the shinyapp.

We're working on an amazon shinyapp as well. watch this space for updates.

Update: Turns out Amazon pages are now dynamic. They were regular pages till last year. So no shinyapp happening on it.

Text analyze the corpus for insights.

Not everything we can do is up on shiny. Would help massively if at least one member per group runs the classwork R code successfully on their machines.

Homework part 2: (Individual submission - option 1)

Use tm.plugin.webmining to pull data from any of the following news aggregators. Pick any product/ firm/ brand/ celebrity that has been in the news lately.

Pull the last 100+ news articles wherein this entity was mentioned in the article title.

Recall the classroom example wherein we did this for Zara:

install.packages("tm") # if using for the first time install.packages("tm.plugin.webmining") library(tm) library(tm.plugin.webmining) # Note: Run below on base R, not RStudio zara <- WebCorpus(GoogleNewsSource("Zara")) x1 = zara # save the corpus in a local file x1 = unlist(lapply(x1, content)) # strip relevant content from x1 x1 = gsub("\n", "", x1) # remove newline chars x1[1:5] # view content write.table(x1, file.choose(), row.names=F, col.names=F) # save file as 'zara_news.txt'

Replace 'zara' above with whatever entity you chose.

Alternately, try running this shiny app for googlenews pulls. Its not very stable but will do for now.

Text-analyze the corpus for sentiment.

Note: Do you see how the corpus thus obtained can potentially help you mine, measure and score some notion of "PR buzz" for the entity?

Your task: ID the two most positive and two most negative articles.

In a PPT slide or two, write what you found about the reasons for positive and negative sentiment.

Update: Pls insert the following lines of code after you run the older code for sentiment analysis.

This is to obtain the most positive and negative documents.

############### head(pol$all[(order(pol$all[,3], decreasing=T)),]) #– Top positive polarity document head(pol$all[(order(pol$all[,3], decreasing=F)),]) #- Top negative polarity document ##################

Homework part 2: (Individual submission - option 2)

Alternately, instead of HW part 2 above, you could do the following.

Take any long (as in 10+ pages) soft copy article that you know and have read.

Use the textsplit shiny app to split it into uniform length parts (of say 25-50 words each).

Now, text anaylze the split document for topics using the shinyapp for topic mining.

In a PPT, paste the wordclouds for each topic and write your interpretation for what that topic means (in a few descriptive words, is all).

Deliverables and Deadlines:

The deadline for this session's HWs is a week from now. Next week Friday (26-sept) midnight.

Drop boxes will be up for session 5 HW part 1 and HW part 2 separately.

For both homework parts, pls submit a zipped folder containing (a) the text dataset you used, and (b) the PPT you made.

Pls remember to write your (group) name and PGID on the title slide. Name the PPT as name_HWnumber.pptx

Added later: The PPT should be <10 slides in length. Feel free to add more slides in an annexure, if required.

The HWs are all HCC level 0. Feel free to take any help from anybody as required.

Any queries etc, contact me.

Ciao.

Sudhir