Leveraging social media


     Indrani Chakravarty




                       © Indrani Chakravarty
Massive volumes of unstructured data


•    Social Networks: FaceBook, Twitter, LinkedIn, …
•    Online stores: amazon, wa...
How can businesses benefit
             from Social Media


• Leveraging Social Media for extracting meaningful
  feedback...
Leveraging Social Media
for extracting meaningful feedback

                     Social Media


  opinions                ...
Underlying Technology -- an approach
                                                                         Linguistic
 ...
Leveraging Social Media for
                      better targeting

                                   Targeting info     ...
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Leveraging Social Media

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Thoughts on how businesses can leverage social media to extract meaningful feedback and to generate superior recommendations and targeting.

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Leveraging Social Media

  1. 1. Leveraging social media Indrani Chakravarty © Indrani Chakravarty
  2. 2. Massive volumes of unstructured data • Social Networks: FaceBook, Twitter, LinkedIn, … • Online stores: amazon, wal-mart, e-bay, … • Blogs • Emails • SMS © Indrani Chakravarty
  3. 3. How can businesses benefit from Social Media • Leveraging Social Media for extracting meaningful feedback • Leveraging Social Media for better targeting © Indrani Chakravarty
  4. 4. Leveraging Social Media for extracting meaningful feedback Social Media opinions listen Analytics Platform Users Filter out noise, analyze/classify sentiments for each feature of the Sells Direct product & give a summary products feedback & recommendations to product/brand/sales management Marketing/Sales Actionable summary/ recommendations © Indrani Chakravarty
  5. 5. Underlying Technology -- an approach Linguistic pre-processing: 1)token normalization Collect adequate training 2) stop-words removal data(e.g. tweets) Tokenize 3) Stemming on a given topic. Hand-label the tweets 4) POS tagging tweets based on sentiment 5)N-gram analysis Apply learning algorithms to learn the model that fits the Compute TF-IDF weight relationship between Store tokens & for each token “i” the attribute set & the class-label corresponding in each tweet “j” in the training data posting lists in (example: SVM/ANN/ inverted index Bayesian/Nearest Neighbor) Test the model on fresh & unseen relevant tweets for sentiments. © Indrani Chakravarty
  6. 6. Leveraging Social Media for better targeting Targeting info Analytics platform Developers Advertisers 1. Cluster users based on: a) demographic & geo-information b) behavioral information, e.g. target • active/passive users ads • category of apps played most click • category of ads clicked most users play listen • type of products on which they apps give opinions most provide opinions interact/ 2. Offer recommendation/ share with Friends/ targeting (apps/ads) Network (social graph) Social Media © Indrani Chakravarty

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