Social Media AnalyticsBy

Mohan Kumar .B

Introduction:• Social media analytics is collecting

data from social media like Twitter, News, Blogs, Videos, etc. and analyse the data.• Social media analytics is useful when

we want to understand market, analyse and others customer service activities.• This is commonly used for sentiment

analysis.

Challenges in Social Media Analytics:• Big Data is the first challenge because handling huge amount of data

is difficult.• Structured and Unstructured Data which is uncommon form of data.• Lot of noise data, which are not really required for analysis.• Complex system architecture to store big data.

Advantages of Social Media Analytics:• Most of the people today use social media for many cases.• For instance, people use social media to review launched product,

share there experience of usage with the product etc.• Product owner wants to know what customers are talking about them

or about the products on which people talking.• Social media analytics can help in this kind of situations.

Problem we have now:• Recently in India, to fight corruption Prime Minister announced that

Rs.500 and Rs.1000 denomination notes are just paper from 10th November 2016. New Denomination Rs.2000 and new Rs.500 notes will be distributed in banks on replacement.• I as a citizen wants to know how people are reacting for this historical

step.

Lets think about it…..

About IBM WATSON for Social Media:• IBM WATSON for Social Media analytics has made this analysis

simpler.• It will extract data from its data aggregator which is embedded with

various social media API to pull data into database.• IBM WATSON is AI which will analyse the data and it can recommend

or report us based on the input we give.

Model Building using IBM WATSON:• First step to build model, one should identify the key words which is

relevant to pull data from data aggregators.• In our case, Key words like RS.500 notes, Rs.1000 Notes, Rs.2000Rs

notes, Black Money, Corruption, Modi, Denomination.• Then identifying and neglecting the non relevant result that could

make the model accuracy less.• Set themes for Model, date limits, languages, source of extraction.

Once Topic, Themes, date, language and source is set. We can start build the model.

Topics Weight

Themes Mentions

Sentiment Analysis

Geographic Analysis

Source

Active Person and Pages

Top Website

Demographics

Conclusion:• We done a sentiment analysis, we found that negative rating are there.

We can do further analysis on negative comments and try to help peoples solve their confusions.• We see that most of the people from Maharashtra has mentioned the

key words.• Few blocks and people, who mentioned about the keyword we

searched.• Twitter is the place were most of the conversation is made on

government decision.• Male peoples used most number of mentions.