EXPERIMENT NO 1

Aim: To Study various social media platforms, analytics tool & techniques, and
Business Application

Software: Youtube Analytics, Google Analytics

Theory:

This experiment will help in developing a basic understanding of social media platforms.
In addition, students will be exposed to various analytics tools and techniques widely
used for analysis and visualization of performance metrics. Students will understand the
importance of social media analysis in various businesses. The objectives are listed as
follows:
i) Learn the basic interface of popular Social Media platforms (Facebook, twitter,
youtube,
etc.).
ii) Use widely available Social Media analytics tools (facebook insights, google
analytics
netlytic, etc.)
iii) Perform Social Media Analytics and learn various techniques and important
engagement
metrics using google analytics, netlytic, lexalytics, etc.)
iv) Study the importance of social media analytics for any business.

Procedure:

Objective 1: Create a user account on any three social media platforms and explore its
Features.

Objective 2: If you have already created pages/content on Instagram or Facebook, use

the 'insights' feature to learn several statistics regarding how the content has been
viewed or has performed over time. Try logging in to Google Analytics Demo Account
from your personal Gmail account and analyze the performance of "Google
Merchandise Store". Share snapshots of the same here!

Objective 3: (i) Access NLP Demo from lexalytics.com website and analyze available
sample texts regarding article/reviews, feedback, etc. (ii) Login to netlytic.org and
analyze the performance of any youtube video, keyword on twitter, google sheets, RSS
feed, reddit data.
Conclusion:
Characteristics of Instagram:
1) Multiple filters can be used for entertainment purpose.
2) Stories Highlight can be important for memories.
3) Video posts can be used as most successful marketing tool.
4) Augmented reality based filters are used for superimposing futuristic, unique and
interactive experiences.
5) IGTV is Instagram's standalone video platform to grow following and increase
engagement.

Characteristics of Twitter:
1) Tweets are the most important feature of Twitter. It allows users to share their
opinion/thoughts regarding a certain topic.
2) Users can pay a subscription fee to get their accounts verified.
3) Twitter's trending page gives the user realtime information about things which are
happening in the world.
4) Twitter thread feature is used by users to give information about in multiple tweets or the
users uses this feature to have conversation over it.
5) Retweets features are used by users to share a particular tweet from different user on
their profile.

Importance of Social Media Analytics:

1. They help you understand your audience

2. They show you what your best social networks are
3. Social data can help you create better content
4. Better Business Insights and Decisions.
Output:
Youtube:

Figure 1: Youtube user

interface which is showing its
feature of videos viewing,
posting and subscription.

Figure 2: Youtube community

group features showing
trending sports followed and
viewed.

Twitter:

Figure 1: Twitter UI for

posting tweets and
What’s happening section
 Figure 2: Trending page of Twitter
showing the current happenings in the
world.

Instagram:

Figure 1: Users profile

and his following on
Instagram

Figure 2: Instagram
stories Feature.

Google Analytics:
 Figure 1: Performance report of a Business
which is registered on Google.

Figure 2: Number of people who viewed the business through various social media

.Instagram analytics:
Analyzing and retrieving data using various online tools

Figure 1: Sentiment
analysis of a review
given on the
.restaurant

Figure 2: Retrieving
comment data of a
youtube video using
netlytic.

Figure 3: Using Netlytic

for retrieving data from
youtube.
 EXPERIMENT NO 2

Aim: To Collect Data from Social Media platforms and popular websites of various
businesses.

Software: Google Chrome Web Scrapper

Theory:

Since the beginning of the web, web scraping has been the main challenge for anyone
who wanted to exploit the richness of information available on the Internet. In the very
beginning, very few APIs were available and people used to copy the content of
websites by just using copy-paste schema. Then, some programmatic tools were
created to follow links (crawling) and extract the content from web pages (scraping).
The information was structured by using text patterns (regex) or DOM (Document
Object Model) parsing methods. More recently, the development of semantic analysis
tools and artificial intelligence enabled alternative approaches, which are much more
efficient and closer to human understanding and interpretation of website content.
Search engines, especially Google, have been leaders in web scraping, as they go
about crawling the entire web to index content from web pages and make it available
through its search engine for the entire world. However, nowadays scraping is an
essential component of many applications of unstructured web content for natural
language processing and other requirements. Some of the features of web crawling and
scraping are listed in TABLE l.
Procedure:

Conclusion:
In this experiment we have studied about difference between Web Crawling and Web
Scraping. Also we done web scraping of different websites using google chrome
extension.

Output:
 EXPERIMENT NO 3

Aim: To understand the importance of data cleaning by pre-processing, filtering, and

visualization of data from the social media platform Twitter using python.

Software: Google Colab

Theory:

The analysis is divided into four parts:

1. Importation & cleaning
2. Visualisation with WordCloud
3. Obtaining Tweet's sources
4. Sentiment analysis

1. Importation & cleaning

In this section we are going to focus on the most important part of the analysis. In
general rule the tweet are composed by several string that we have to clean before
working correctly with the data. I have separated the importation of package into four
parts. Usually numpy and pandas are part of our toolbox.
For the visualisation we use Seaborn, Matplotlib, Basemap and finall word_cloud. In
order to clean our data (Text) and sentiment analysis the most common library is NLTK.
NLTK is a leading platform Python programs to workin with human language data. It
exists another Natural Language Toolkit (gensim) but in our case it is not necessary to
use it.
The data with which are going to work is a list of tweets with the hashtag #goodmorning.

2. Visualisation with WordCloud

Throughout this part we are going to focus on the kind of data visualisation word cloud.
It always interesting to do this kind of viz in order to have a global vision of the data. The
visualisation are going to do with the column "text" and "country".

3. Tweet's source

In this third part we are going to check the source of the tweets. And by the source I
mean the device and the location. The purpose is to see the repartition of the tweet by
deveice. As usual the first is the cleaning. The kind of device is situated at the end in the
column "source". With the following example we can see that the device is just before
"/a>".
4. Sentiment Analysis

Throughout last part we are going to do an sentiment analysis. The objective is to class
by type th tweets. We are going to distingush 3 kind of tweets according to their polarity
score. We will have the positive tweets, the neutral tweets and the negative tweets.

Result:
Conclusion:
In this way we have studied the importance of data cleaning and pre-processing and
visualization of Twitter data analysis.
 EXPERIMENT NO 4

Aim: To perform exploratory data analysis and visualization of (i) financial data of any
business (ii) social media data of any business.

Software: MS Power BI

Theory:

Exploratory Data Analysis (EDA) is a process of describing the data by means of

statistical and visualization techniques in order to bring important aspects of that data
into focus for further analysis. This involves inspecting the dataset from many angles,
describing & summarizing it without making any assumptions about its contents.
Just like everything in this world, data has its imperfections. Raw data is usually
skewed, may have outliers, or too many missing values. A model built on such data
results in sub-optimal performance. In hurry to get to the machine learning stage, some
data professionals either entirely skip the exploratory data analysis process or do a very
mediocre job. This is a mistake with many implications, that includes generating
inaccurate models, generating accurate models but on the wrong data, not creating the
right types of variables in data preparation, and using resources inefficiently.

Data visualization is the graphical representation of information and data. By using

visual elements like charts, graphs, and maps, data visualization tools provide an
accessible way to see and understand trends, outliers, and patterns in data.
Additionally, it provides an excellent way for employees or business owners to present
data to non-technical audiences without confusion.

In the world of Big Data, data visualization tools and technologies are essential to
analyze massive amounts of information and make data-driven decisions.The
importance of data visualization is simple: it helps people see, interact with, and better
understand data. Whether simple or complex, the right visualization can bring everyone
on the same page, regardless of their level of expertise.

Procedure:

1. Install Power BI Desktop (with official college email IDs only)

2. Import data from an excel sheet or any source of choice.
3. Transform the data (if required) then import.
4. Create different visualizations of the data.
5. Repeat steps 2 to 5 for (i) Financial data (ii) Social Media Data of Twitter.
Result:
 EXPERIMENT NO 5

Aim: To Study sentiments analysis from Youtube comments.

Software: Google Colab

Theory:

Sentiment analysis, also referred to as opinion mining, is an approach to natural

language processing (NLP) that identifies the emotional tone behind a body of text. This
is a popular way for organizations to determine and categorize opinions about a
product, service or idea. Sentiment analysis involves the use of data mining, machine
learning (ML), artificial intelligence and computational linguistics to mine text for
sentiment and subjective information such as whether it is expressing positive, negative
or neutral feelings.
Sentiment analysis systems help organizations gather insights into real-time customer
sentiment, customer experience and brand reputation. Generally, these tools use text
analytics to analyze online sources such as emails, blog posts, online reviews,
customer support tickets, news articles, survey responses, case studies, web chats,
tweets, forums and comments. Algorithms are used to implement rule-based, automatic
or hybrid methods of scoring whether the customer is expressing positive words,
negative words or neutral ones.

Procedure:
Sentiment analysis generally follows these steps:
Collect data. The text being analyzed is identified and collected. This involves using a
web scraping bot or a scraping application programming interface.
Clean the data. The data is processed and cleaned to remove noise and parts of
speech that don't have meaning relevant to the sentiment of the text. This includes
contractions, such as I'm, and words that have little information such as is, articles such
as the, punctuation, URLs, special characters and capital letters. This is referred to as
standardizing.
Extract features. A machine learning algorithm automatically extracts text features to
identify negative or positive sentiment. ML approaches used include the bag-of-words
technique that tracks the occurrence of words in a text and the more nuanced
word-embedding technique that uses neural networks to analyze words with similar
meanings.
Pick an ML model. A sentiment analysis tool scores the text using a rule-based,
automatic or hybrid ML model. Rule-based systems perform sentiment analysis based
on predefined, lexicon-based rules and are often used in domains such as law and
medicine where a high degree of precision and human control is needed. Automatic
systems use ML and deep learning techniques to learn from data sets. A hybrid model
combines both approaches and is generally thought to be the most accurate model.
These models offer different approaches to assigning sentiment scores to pieces of text.
Sentiment classification. Once a model is picked and used to analyze a piece of text, it
assigns a sentiment score to the text including positive, negative or neutral.
Organizations can also decide to view the results of their analysis at different levels,
including document level, which pertains mostly to professional reviews and coverage;
sentence level for comments and customer reviews; and sub-sentence level, which
identifies phrases or clauses within sentences.

Result:
 EXPERIMENT NO 6

Aim: To perform social network analysis of ‘Facebook’ data.

Software: Python Colab

Theory:

Social network graphs

Featured snippet from the web
A social graph is a diagram that illustrates interconnections among people, groups and
organizations in a social network. The term is also used to describe an individual's
social network. When portrayed as a map, a social graph appears as a set of network
nodes that are connected by lines.
The Social Network graph shows:
​ Entity-to-entity links: You see all the entities related to the main (hub) entity. However,
the attributes that link the entities do not display on the graph but are accessible by
using the Attribute Explorer in combination with the graph.
​ Relationship clusters: The Social Network graph is unique in that it displays the related
entities in groups or clusters. This graph can help you see all the relationship clusters
a particular entity belongs to and look for patterns in among the clusters and
relationships.

You can expand the graph to show all the related entities for any entity. Each time you
show all entities related to a particular entity, that entity node becomes the hub entity in
a new relationship cluster.To maintain the integrity of each relationship cluster, an entity
can be displayed on the graph multiple times in multiple relationship clusters. But each
entity displays in each relationship cluster only once. To see every relationship cluster
the entity is part of, select the entity by clicking on that node. The interior of the selected
entity node changes to blue in each relationship cluster that the entity is part of.
Procedure:

Clustering of Social-Network Graphs:

Clustering of the graph is considered as a way to identify communities. Clustering of

graphs involves following steps:

Degree Centrality

The people most popular or more liked usually are the ones who have more friends.
Degree centrality is a measure of the number of connections a particular node has in
the network. It is based on the fact that important nodes have many connections.
NetworkX has the function degree_centrality() to calculate the degree centrality of all
the nodes of a network.

Eigenvector Centrality

It is not just how many individuals one is connected too, but the type of people one is
connected with that can decide the importance of a node. In Delhi Roads whenever the
traffic police capture a person

Betweenness Centrality
The Betweenness Centrality is the centrality of control. It represents the frequency at
which a point occurs on the geodesic (shortest paths) that connected pair of points. It
quantifies how many times a particular node comes in the shortest chosen path
between two other nodes. The nodes with high betweenness centrality play a
significant role in the communication/information flow within the network. The nodes
with high betweenness centrality can have a strategic control and influence on others.
An individual at such a strategic position can influence the whole group, by either
withholding or coloring the information in transmission. Networkx has the function
betweenness_centrality() to measure it for the network. It has options to select if we
want betweenness values to be normalized or not, weights to be included in centrality
calculation or not, and to include the endpoints in the shortest path counts or not.

Result:
Conclusion:
In this way we have studied the importance Social Network Analysis using Facebook
data.
 EXPERIMENT NO 7

Aim: To Create a dashboard in PowerBI for Superstore Dataset.

Software: MS PowerBI

Theory:

Power BI offers interactive and dynamic features required for creating interactive
dashboards. These dashboards, which are simply a collection of visuals, can be built
with a deep level of interactivity and are accessible in various formats to consumers.
Since they are usually a single page, Power BI dashboards need to be well-designed
highlights of an entire story.
It is also important to note that Power BI dashboards are quite different from Power BI
reports. For example:
Power BI reports are available on Power BI Desktop and Power BI service, while Power
BI dashboards can only be found on Power BI service.
Reports can be multi-paged, while dashboards are single-paged highlights.

Procedure:

Importing Data

The first step in building a Power BI dashboard is to import the dataset that will be used
to build the report. You can connect to a variety of data sources, including Excel
worksheets, databases, the web, and cloud services.

Opening a Report from the Uploaded Data

Notice that from the workspace image, there are two file types of the same name. One
is the dataset and the other is the dashboard.

Click on the Superstore.xlsx dashboard. On the blank canvas that pops up, click on the
dataset name ‘Superstore.xlsx’.

Creating a Tile and ‘Pin to a Dashboard’

Power BI report service, just like the desktop version, includes a variety of page
formatting options, including visuals, shapes, and images, that can help your report
stand out. One of the most efficient ways to identify and communicate insights is to use
Power BI to create visuals.

Result:

Conclusion:
In this way we have studied the importance of DashBoard using PowerBI
 EXPERIMENT NO 8

Aim: To design the creative content for promotion of your business on social media
platform.

Software: Canva, Visme or free version of any social media ad making tool.
Theory:

Social media Marketing:

Social media marketing for small business is all about being strategic. While enterprise
companies have the luxury of dedicated resources and time, small businesses need to
be more agile, nimble, and creative.

Why use social media for your small business

If you own a business, you’ve likely spent time researching social media marketing for
small business. And for good reason.There are now 4.2 billion active social media
users. That’s almost twice as many as there were just five years ago, in 2017. Those
users spend an average of 2 hours and 25 minutes on social channels every single day.

Reach more potential customers

Increase your brand awareness

Understand your customers better

Understand your competitors better

Build long-term relationships with your customers

Result:
1. Apple: "Get a Mac" Campaign
The "Get a Mac" campaign was a series of television commercials created by Apple Inc.
to market its Macintosh computers. The ads were directed at Windows users and
featured two actors personifying a PC and a Mac.
The campaign began in May 2006 with the release of the "Mac vs. PC" commercial, and
ended in October 2009 with the release of the "I'm a Mac" commercial. The ads were
produced by TBWA\Media Arts Lab, the advertising agency responsible for all of Apple's
advertising.

2. Nike: "Just Do It" Campaign

Nike's "Just Do It" campaign is one of the most iconic and successful advertising
campaigns of all time. The simple yet powerful slogan has inspired athletes and
non-athletes alike to push themselves to their limits and achieve their goals.The
campaign was created in 1988 by advertising agency Wieden+Kennedy, and the first ad
featured former track and field athlete Steve Prefontaine. The ad showed Prefontaine
running along a beach and ended with the slogan "Just do it." The ad was a huge
success, and Nike quickly adopted it as their official slogan.

3. Fitbit: “Find Your Reason” Marketing Campaign Inspires Users to Take Action

What better form of marketing is there than powerful and inspiring success stories from
real people? Fitbit’s “Find Your Reason” campaign showcases the unique stories from
individuals who incorporated a Fitbit into their life and changed their life for the better.
One woman tells the story of how she lost 79 pounds and kicked diabetes out the door
while another man discusses how his sleep improved dramatically, which in turn allowed
him to be a better person.
Output:

Sunglasses Ad Campaign

Conclusion:
In this way we have studied the importance of Social Marketing by creating Sunglasses
Ad using Canva.
 EXPERIMENT NO 9

Aim: To analyze competitor activities using social media data.

Software: Competitor analysis web tools such as ‘Similarweb.com’, ‘Semrush.com’,

etc.

Theory:

Traffic and Engagement:

In the context of website performance insights, traffic refers to the number of visitors
that a website receives. This metric is often used to measure the popularity and reach of
a website. It can also provide valuable insights into user behavior, such as where they
are coming from and what pages they are visiting. Engagement, on the other hand,
refers to the level of interaction and involvement that visitors have with a website. This
can include metrics such as the average time spent on site, the number of pages
viewed per visit, and the bounce rate (the percentage of visitors who leave after only
viewing one page). Engagement metrics can provide insights into the effectiveness of a
website's design, content, and user experience. Both traffic and engagement are
important metrics for understanding website performance. A website with high traffic but
low engagement may indicate that visitors are not finding what they are looking for,
while a website with low traffic but high engagement may indicate that the website is
meeting the needs of a niche audience. By analyzing both traffic and engagement
metrics, website owners and managers can gain valuable insights into how to improve
the user experience and drive more meaningful interactions with their audience.

Marketing Channels:
In the context of website performance insights, marketing channels refer to the various
ways that visitors find and access a website. This can include organic search, paid
search, social media, email marketing, direct traffic, and referral traffic. Understanding
the performance of each marketing channel can provide valuable insights into the
effectiveness of a website's marketing strategy. For example, if a website is receiving a
high volume of traffic from organic search but low engagement from social media, this
may indicate that the website's content is well-optimized for search engines but may
need improvement in terms of social media marketing. Analyzing marketing channel
data can also help website owners and managers identify opportunities for optimization
and improvement. For example, if a website is not receiving any traffic from a particular
marketing channel, this may indicate that the website's marketing efforts in that channel
need to be reevaluated or adjusted. In summary, marketing channels play a critical role.
Organic/Paid Search Terms:
In the context of website performance insights, organic and paid search terms refer to
the keywords that visitors use to find a website through search engines like Google,
Bing, or Yahoo. Organic search terms are the keywords that visitors use to find a
website through unpaid, natural search results. These are the search results that
appear below the paid ads on a search engine results page. Analyzing organic search
terms can provide valuable insights into the effectiveness of a website's search engine
optimization (SEO) strategy, as well as the keywords that are most relevant to the
website's content. Paid search terms, on the other hand, are the keywords that visitors
use to find a website through paid search ads. These are the ads that appear at the top
or bottom of a search engine results page, marked as "sponsored" or "ad." Analyzing
paid search terms can provide valuable insights into the effectiveness of a website's
search engine marketing (SEM) strategy, as well as the keywords that are most
effective in driving paid traffic to the website. By analyzing both organic and paid search
terms, website owners and managers can gain valuable insights into the keywords that
are driving traffic to their website and how to optimize their search engine strategy for
better performance.

Social Traffic:
In the context of website performance insights, social traffic refers to the visitors that
come to a website through social media platforms such as Facebook, Twitter, LinkedIn,
Instagram, Pinterest, and others. Social traffic is an important metric for website owners
and managers to track, as it can provide insights into the effectiveness of a website's
social media marketing strategy. By analyzing social traffic data, website owners and
managers can gain insights into which social media platforms are driving the most traffic
to their website, as well as which social media campaigns and content are most
effective in driving engagement and conversions.

Display Traffic Overview:

In the context of website performance insights, display traffic refers to the visitors that
come to a website through display advertising, which includes banner ads, video ads,
and other types of visual advertising. Display traffic overview provides an overall view of
the performance of a website's display advertising campaigns. It includes metrics such
as impressions (the number of times an ad is displayed), clicks (the number of times an
ad is clicked), click-through rate (CTR), and conversion rate (the percentage of visitors
who take a desired action after clicking on an ad).
Result:
1. Flipkart vs Amazon (SimilarWeb.com):
1. Twitter vs Facebook (SimilarWeb.com):
Conclusion:
In this experiment we have studied about how to analyze Social Media websites and
compare their analytics using online tools like similarweb.com and semrush.com