This document analyzes Twitter sentiment regarding Indian e-commerce companies' festive sales in 2015. It extracts tweets related to Flipkart, Amazon, and Snapdeal's sales and uses lexicon-based sentiment analysis in R to determine the sentiment polarity of each tweet. The analysis found that Snapdeal had more positive sentiment both before and during the sales, while all three companies received positive sentiment after the sales. The results validated reports that Snapdeal was customers' favorite during this period.

1. LEXICON BASED TWITTER SENTIMENTAL ANALYSIS OF
INDIAN E-COMMERCE FESTIVE SALE - 2015

2. E-commerce in India
• Electronic commerce, commonly written as e-commerce, is the trading in products or services using
computer networks, such as the Internet.
• India has an internet user base of about 354 million as of June 2015.
• India's retail market is expected to grow to $675 Bn by 2016 and $850 Bn by 2020
• Despite being third largest user base in world, the penetration of e-commerce is low compared to
markets like US or UK, but is growing much faster, adding around 6 million new entrants every month.
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3. What is Sentiment Analysis
• Sentiment analysis (also known as opinion mining) refers to the use of natural language
processing, text analysis and computational linguistics to identify and extract subjective information in
source materials.
• Aims to determine the attitude of a speaker or a writer with respect to some topic or the overall
contextual polarity of a document.
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4. Social media analytics
• Social Media tools like Twitter becomes the new means of communication, with it, millions of people
express anything and everything within the 140-character constraint.
• One example of a milestone event for the Social Media world was the 2012 London Summer
Olympics. This was the first Olympic event that leveraged Big Data analytics in a major way to
understand every sentiment, capture every expression and demonstrate trends in real time.
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5. Festive Sale 2014 – An Overview
• The Diwali Festive Sale is the most anticipated event for Indian Ecommerce Industry
• It all started with the Big Billion Day by Flipkart in 2014, which saw the biggest ever turnover
in Indian E-Commerce history
• Though Flipkart had high turnover during the period, negative sentiments started to flow
against Flipkart, due to the various reasons viz. technical glitches, high price, out of stock
messages etc.
• These negative sentiments created a world of opportunity for its competitors such as
Amazon and Snapdeal to jump into the scene
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6. Festive Sale 2015 – An Analysis
• Compared to 2014, Ecommerce platforms were better equipped at handling the incredible
scale of Indian consumers this festive season.
• With additions like App Only Sale, new offers on hourly basis, investment into analytics, big
data and logistics, Ecommerce companies have mostly hit all the right notes to keep
consumers and their sellers happy.
• Social media like Twitter have been the happening place for all the customer feeling and
sentiments
• We here have provided the analysis of tweets of companies based on sentiments using R
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7. Integrating R with Twitter API
R integration with the twitter is done using the twitter API.
In order to connect, one needs the following keys, which are available to any twitter user.
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8. Data Extraction
• twitteR makes searching Twitter as simple as can be.
• Once the package is loaded, one line is all you need to search Twitter
and fetch up to 10000 results:
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9. Exploratory Data Analysis
In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their
main characteristics, often with visual methods. A statistical model can be used or not, but primarily
EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task
We try to understand the twitter data
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10. Data Cleansing
The tweets have to cleaned in order for text mining, this is done by removing special characters,
punctuations, hyperlinks etc…
Note: Retweets also have to removed since they are repeatitions here.
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11. Error Handling
Even after using regex function to remove irregularities in the tweets, there will be values
which cannot be processed by sentimental analysis function, the solution to this is by using
error handler.
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12. Creating a Positive/Negative Lexicon
The link of source file for positive and negative lexicon is given below:
www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
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13. Text Mining by Applying Sentiment Analysis
• Sentiment analysis is an active area of research involving complicated algorithms and subtleties.
• We are estimating a tweet’s sentiment by counting the number of occurrences of “positive” and
“negative” words.
• To assign a numeric score to each tweet, we’ll simply subtract the number of occurrences of
negative words from the number of positive. Larger negative scores will correspond to more
negative expressions of sentiment, neutral (or balanced) tweets should net to zero, and very
positive tweets should score larger, positive numbers.
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14. Sample
Below is a sample output for sentiment analysis of the data provided.
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15. Visualizing the Results
After scoring each tweet, an overall score is computed and the result is visualized using a
histogram.
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16. Analysis Before Sales
Analyzing the tweets before the sales provides us a glimpse of customers’ sentiments before sales
Before the sales, the
sentiments favored
Snapdeal comparatively
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17. Analysis During Sales
Analyzing the tweets during sales provides us a glimpse of customers’ sentiments on the days of
Sale
During the sales, Snapdeal
has maintained its position
As the customer favorite.
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18. Analysis After Sales
Analyzing the tweets after sales provides us a glimpse of customers’ sentiments
All three companies
received positive
sentiments after the festive
sales
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19. Media Reports Our Analysis have
been validated
with the
newspaper
articles on the
same
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20. References
 Jeffrey Breen ( October 25, 2011) - Mining Twitter for Airline Consumer Sentiment
 Gaston Sanchez - Sentiment Analysis with "sentiment"
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