2. 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.
3. 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.
4. 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.
5. 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.
7. 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.
8. 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.
9. Once Topic, Themes, date, language and source is set. We can start
build the model.
18. 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.
