The Six Step Formula to be the Soonicorn Unicorn

There was a time when a company with a billion-dollar valuation was an experience one and not a start-up. And today, you will see more start-ups who rose to fame much earlier than expected. They are the unicorns.
The Unicorns are young, more assorted, and undergo faster growth than others. They are multiplying at an astounding rate. As of 2019, there are more than 400 companies that are categorized as unicorns. This is a good number for more aspiring entrepreneurs who dream to be the next Unicorn.

Therefore, we are presenting a list of patterns followed by some of the high-valued Unicorns. The list covers what they do differently to stand out from all and how they accomplish such unparalleled success.
Let’s dive in.

**The Power of Five - Blueprint of being the Next Unicorn

Step 1: Commit to Rapid Growth

There is a difference in being a start-up and being a unicorn. In order to be remarkably successful as a tech founder, you must have some qualities;
Be unusually driven to create something disruptive.
List your company’s core values, mission, and culture.
Believe in your product first or the service that you offer will drastically improve EU’s situation.
Create a revenue-based business model that aims at drawing profit from the first day.

Step 2: Create an MVP (Minimum Viable Product)

An MVP is ideally the first step towards your goal. Every Unicorn has started their journey of being the market disruptors by creating a minimum viable product (MVP). In other words, it is a modest, scalable contribution that stands as a solution to a real-world problem. Most importantly, MVP’s are ought to be unique than what others are offering. You simply cannot become a unicorn by re-designing a better vision of something that is already existent.

You should have a burning desire to disrupt. This could either mean histrionically improving an existing solution or innovating path-breaking product into the market.

Bigger isn’t better always, especially when it comes to tech, a disruptive tech. To clarify, customization and specialization are often a healthier gamble than products with an abrupt mass-appeal.

Most importantly, start with small. Narrow down your target market. Don’t simply enter because it’s huge. Chances are your start-up may be lost in a densely populated vertical. Relax! The idea is not to scare you but to help you understand the basics and the mistake that you should avoid.
Key Takeaway - Discover your niche, prove your concept by targeting a smaller section of the market, and then scalable towards growth.

Step 3: Build the Right Product

Once you have decided on a product, plan a glidepath on building it right. Product development is no easy procedure, but some of the most fruitful ideas started from small, from failure and a determination to turn the tables in their favour. Experiment, observe, fall, start over, and repeat until your customer engagement and user experience are completely aligned with your business goals.

Check if your idea has unicorn-level market potential for

• Achieving effortless product-market by testing your product/service with ease.
• Creating excitement about your product/service and consumers are ready to buy.
• Manufacturing and producing the product/service in bulk.
• Serving global market, if scaled fast.
• Winning with a team of aces and industry-leading experts.
• Being "disruptive." You are the only one in the game.

Key Takeaway: Unicorns test their conventions, target huge market segments, and look for key indicators and opportunities.

Step 4: Secure financial backing

Let’s be honest. It all starts with money. Cashflow is the essence of any business. Many start-ups function on a “revenue first, profitability later” model which paves the way to downhill. Next mistake they make is approaching investors before taking off.

You have to understand, investors only invest in potential unicorns. They look to invest in start-ups who have already demonstrated their capability to scale and achieve great heights if backed financially.
So, bootstrap or loan from friends, start off. It is an absolute necessity show traction to seek investments. Pin down your key sales metrics to quantify your growth. This can also help in further product development and justify additional rounds of funding.

Key Takeaway: Focus on financial and sales metrics, and provide documents that show traction and proves your ability to replicate the results even better on a larger platform.

Step 5: Select a Space to Scale

As you start from small and as your team expands, it is essential to scale with the help of companies that are committed help other companies scale. Mobulous is a company who shares the same vision like you – Your company’s success. Mobulous is an emerging tech ecosystem that is backed by latest technology and team of aces, hackers and hustlers. With various ways to help you grow, Mobulous is your ideal go-to company.
Many of the unicorns make wise use of the power of technology to achieve colossal growth. At Mobulous, backed by strong company culture and agile methodology, we help businesses reach their goals.

Key Takeaway: An important aspect of becoming a unicorn is knowing how to make use of the right people and take help from the right resources.

Scale with Mobulous

Only two percent of start-ups turn unicorn. See the brighter side — it’s possible. Have a burning desire, build the right product, start small, focus on growth, seek top investors, drive outstanding company culture and choose the right company to help you grow, and increase the chances of being the next unicorn. Every right decision will take you towards your goal. Take your first step. Choose Mobulous and leave the rest to us.

#mobile #app #mobile-apps

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The Six Step Formula to be the Soonicorn Unicorn
John  Smith

John Smith


Find the Best Restaurant Mobile App Development Company in Abu Dhbai

The era of mobile app development has completely changed the scenario for businesses in regions like Abu Dhabi. Restaurants and food delivery businesses are experiencing huge benefits via smart business applications. The invention and development of the food ordering app have helped all-scale businesses reach new customers and boost sales and profit. 

As a result, many business owners are searching for the best restaurant mobile app development company in Abu Dhabi. If you are also searching for the same, this article is helpful for you. It will let you know the step-by-step process to hire the right team of restaurant mobile app developers. 

Step-by-Step Process to Find the Best Restaurant App Development Company

Searching for the top mobile app development company in Abu Dhabi? Don't know the best way to search for professionals? Don't panic! Here is the step-by-step process to hire the best professionals. 

#Step 1 – Know the Company's Culture

Knowing the organization's culture is very crucial before finalizing a food ordering app development company in Abu Dhabi. An organization's personality is shaped by its common beliefs, goals, practices, or company culture. So, digging into the company culture reveals the core beliefs of the organization, its objectives, and its development team. 

Now, you might be wondering, how will you identify the company's culture? Well, you can take reference from the following sources – 

  • Social media posts 
  • App development process
  • About us Page
  • Client testimonials

#Step 2 - Refer to Clients' Reviews

Another best way to choose the On-demand app development firm for your restaurant business is to refer to the clients' reviews. Reviews are frequently available on the organization's website with a tag of "Reviews" or "Testimonials." It's important to read the reviews as they will help you determine how happy customers are with the company's app development process. 

You can also assess a company's abilities through reviews and customer testimonials. They can let you know if the mobile app developers create a valuable app or not. 

#Step 3 – Analyze the App Development Process

Regardless of the company's size or scope, adhering to the restaurant delivery app development process will ensure the success of your business application. Knowing the processes an app developer follows in designing and producing a top-notch app will help you know the working process. Organizations follow different app development approaches, so getting well-versed in the process is essential before finalizing any mobile app development company. 

#Step 4 – Consider Previous Experience

Besides considering other factors, considering the previous experience of the developers is a must. You can obtain a broad sense of the developer's capacity to assist you in creating a unique mobile application for a restaurant business.

You can also find out if the developers' have contributed to the creation of other successful applications or not. It will help you know the working capacity of a particular developer or organization. Prior experience is essential to evaluating their work. For instance, whether they haven't previously produced an app similar to yours or not. 

#Step 5 – Check for Their Technical Support

As you expect a working and successful restaurant mobile app for your business, checking on this factor is a must. A well-established organization is nothing without a good technical support team. So, ensure whatever restaurant mobile app development company you choose they must be well-equipped with a team of dedicated developers, designers, and testers. 

Strong tech support from your mobile app developers will help you identify new bugs and fix them bugs on time. All this will ensure the application's success. 

#Step 6 – Analyze Design Standards

Besides focusing on an organization's development, testing, and technical support, you should check the design standards. An appealing design is crucial in attracting new users and keeping the existing ones stick to your services. So, spend some time analyzing the design standards of an organization. Now, you might be wondering, how will you do it? Simple! By looking at the organization's portfolio. 

Whether hiring an iPhone app development company or any other, these steps apply to all. So, don't miss these steps. 

#Step 7 – Know Their Location

Finally, the last yet very crucial factor that will not only help you finalize the right person for your restaurant mobile app development but will also decide the mobile app development cost. So, you have to choose the location of the developers wisely, as it is a crucial factor in defining the cost. 

Summing Up!!!

Restaurant mobile applications have taken the food industry to heights none have ever considered. As a result, the demand for restaurant mobile app development companies has risen greatly, which is why businesses find it difficult to finalize the right person. But, we hope that after referring to this article, it will now be easier to hire dedicated developers under the desired budget. So, begin the hiring process now and get a well-craft food ordering app in hand. 

Dylan  Iqbal

Dylan Iqbal


Matplotlib Cheat Sheet: Plotting in Python

This Matplotlib cheat sheet introduces you to the basics that you need to plot your data with Python and includes code samples.

Data visualization and storytelling with your data are essential skills that every data scientist needs to communicate insights gained from analyses effectively to any audience out there. 

For most beginners, the first package that they use to get in touch with data visualization and storytelling is, naturally, Matplotlib: it is a Python 2D plotting library that enables users to make publication-quality figures. But, what might be even more convincing is the fact that other packages, such as Pandas, intend to build more plotting integration with Matplotlib as time goes on.

However, what might slow down beginners is the fact that this package is pretty extensive. There is so much that you can do with it and it might be hard to still keep a structure when you're learning how to work with Matplotlib.   

DataCamp has created a Matplotlib cheat sheet for those who might already know how to use the package to their advantage to make beautiful plots in Python, but that still want to keep a one-page reference handy. Of course, for those who don't know how to work with Matplotlib, this might be the extra push be convinced and to finally get started with data visualization in Python. 

You'll see that this cheat sheet presents you with the six basic steps that you can go through to make beautiful plots. 

Check out the infographic by clicking on the button below:

Python Matplotlib cheat sheet

With this handy reference, you'll familiarize yourself in no time with the basics of Matplotlib: you'll learn how you can prepare your data, create a new plot, use some basic plotting routines to your advantage, add customizations to your plots, and save, show and close the plots that you make.

What might have looked difficult before will definitely be more clear once you start using this cheat sheet! Use it in combination with the Matplotlib Gallery, the documentation.


Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.

Prepare the Data 

1D Data 

>>> import numpy as np
>>> x = np.linspace(0, 10, 100)
>>> y = np.cos(x)
>>> z = np.sin(x)

2D Data or Images 

>>> data = 2 * np.random.random((10, 10))
>>> data2 = 3 * np.random.random((10, 10))
>>> Y, X = np.mgrid[-3:3:100j, -3:3:100j]
>>> U = 1 X** 2 + Y
>>> V = 1 + X Y**2
>>> from matplotlib.cbook import get_sample_data
>>> img = np.load(get_sample_data('axes_grid/bivariate_normal.npy'))

Create Plot

>>> import matplotlib.pyplot as plt


>>> fig = plt.figure()
>>> fig2 = plt.figure(figsize=plt.figaspect(2.0))


>>> fig.add_axes()
>>> ax1 = fig.add_subplot(221) #row-col-num
>>> ax3 = fig.add_subplot(212)
>>> fig3, axes = plt.subplots(nrows=2,ncols=2)
>>> fig4, axes2 = plt.subplots(ncols=3)

Save Plot 

>>> plt.savefig('foo.png') #Save figures
>>> plt.savefig('foo.png',  transparent=True) #Save transparent figures

Show Plot


Plotting Routines 

1D Data 

>>> fig, ax = plt.subplots()
>>> lines = ax.plot(x,y) #Draw points with lines or markers connecting them
>>> ax.scatter(x,y) #Draw unconnected points, scaled or colored
>>> axes[0,0].bar([1,2,3],[3,4,5]) #Plot vertical rectangles (constant width)
>>> axes[1,0].barh([0.5,1,2.5],[0,1,2]) #Plot horiontal rectangles (constant height)
>>> axes[1,1].axhline(0.45) #Draw a horizontal line across axes
>>> axes[0,1].axvline(0.65) #Draw a vertical line across axes
>>> ax.fill(x,y,color='blue') #Draw filled polygons
>>> ax.fill_between(x,y,color='yellow') #Fill between y values and 0

2D Data 

>>> fig, ax = plt.subplots()
>>> im = ax.imshow(img, #Colormapped or RGB arrays
      cmap= 'gist_earth', 
      interpolation= 'nearest',
>>> axes2[0].pcolor(data2) #Pseudocolor plot of 2D array
>>> axes2[0].pcolormesh(data) #Pseudocolor plot of 2D array
>>> CS = plt.contour(Y,X,U) #Plot contours
>>> axes2[2].contourf(data1) #Plot filled contours
>>> axes2[2]= ax.clabel(CS) #Label a contour plot

Vector Fields 

>>> axes[0,1].arrow(0,0,0.5,0.5) #Add an arrow to the axes
>>> axes[1,1].quiver(y,z) #Plot a 2D field of arrows
>>> axes[0,1].streamplot(X,Y,U,V) #Plot a 2D field of arrows

Data Distributions 

>>> ax1.hist(y) #Plot a histogram
>>> ax3.boxplot(y) #Make a box and whisker plot
>>> ax3.violinplot(z)  #Make a violin plot

Plot Anatomy & Workflow 

Plot Anatomy 




The basic steps to creating plots with matplotlib are:

1 Prepare Data
2 Create Plot
3 Plot
4 Customized Plot
5 Save Plot
6 Show Plot

>>> import matplotlib.pyplot as plt
>>> x = [1,2,3,4]  #Step 1
>>> y = [10,20,25,30] 
>>> fig = plt.figure() #Step 2
>>> ax = fig.add_subplot(111) #Step 3
>>> ax.plot(x, y, color= 'lightblue', linewidth=3)  #Step 3, 4
>>> ax.scatter([2,4,6],
          color= 'darkgreen',
          marker= '^' )
>>> ax.set_xlim(1, 6.5)
>>> plt.savefig('foo.png' ) #Step 5
>>> #Step 6

Close and Clear 

>>> plt.cla()  #Clear an axis
>>> plt.clf(). #Clear the entire figure
>>> plt.close(). #Close a window

Plotting Customize Plot 

Colors, Color Bars & Color Maps 

>>> plt.plot(x, x, x, x**2, x, x** 3)
>>> ax.plot(x, y, alpha = 0.4)
>>> ax.plot(x, y, c= 'k')
>>> fig.colorbar(im, orientation= 'horizontal')
>>> im = ax.imshow(img,
            cmap= 'seismic' )


>>> fig, ax = plt.subplots()
>>> ax.scatter(x,y,marker= ".")
>>> ax.plot(x,y,marker= "o")


>>> plt.plot(x,y,linewidth=4.0)
>>> plt.plot(x,y,ls= 'solid') 
>>> plt.plot(x,y,ls= '--') 
>>> plt.plot(x,y,'--' ,x**2,y**2,'-.' ) 
>>> plt.setp(lines,color= 'r',linewidth=4.0)

Text & Annotations 

>>> ax.text(1,
           'Example Graph', 
            style= 'italic' )
>>> ax.annotate("Sine", 
xy=(8, 0),
xycoords= 'data', 
xytext=(10.5, 0),
textcoords= 'data', 
arrowprops=dict(arrowstyle= "->", 


>>> plt.title(r '$sigma_i=15$', fontsize=20)

Limits, Legends and Layouts 

Limits & Autoscaling 

>>> ax.margins(x=0.0,y=0.1) #Add padding to a plot
>>> ax.axis('equal')  #Set the aspect ratio of the plot to 1
>>> ax.set(xlim=[0,10.5],ylim=[-1.5,1.5])  #Set limits for x-and y-axis
>>> ax.set_xlim(0,10.5) #Set limits for x-axis


>>> ax.set(title= 'An Example Axes',  #Set a title and x-and y-axis labels
            ylabel= 'Y-Axis', 
            xlabel= 'X-Axis')
>>> ax.legend(loc= 'best')  #No overlapping plot elements


>>> ax.xaxis.set(ticks=range(1,5),  #Manually set x-ticks
             ticklabels=[3,100, 12,"foo" ])
>>> ax.tick_params(axis= 'y', #Make y-ticks longer and go in and out
             direction= 'inout', 

Subplot Spacing 

>>> fig3.subplots_adjust(wspace=0.5,   #Adjust the spacing between subplots
>>> fig.tight_layout() #Fit subplot(s) in to the figure area

Axis Spines 

>>> ax1.spines[ 'top'].set_visible(False) #Make the top axis line for a plot invisible
>>> ax1.spines['bottom' ].set_position(( 'outward',10))  #Move the bottom axis line outward

Have this Cheat Sheet at your fingertips

Original article source at

#matplotlib #cheatsheet #python

Garry Taylor

Garry Taylor


Python Data Visualization: Bokeh Cheat Sheet

A handy cheat sheet for interactive plotting and statistical charts with Bokeh.

Bokeh distinguishes itself from other Python visualization libraries such as Matplotlib or Seaborn in the fact that it is an interactive visualization library that is ideal for anyone who would like to quickly and easily create interactive plots, dashboards, and data applications. 

Bokeh is also known for enabling high-performance visual presentation of large data sets in modern web browsers. 

For data scientists, Bokeh is the ideal tool to build statistical charts quickly and easily; But there are also other advantages, such as the various output options and the fact that you can embed your visualizations in applications. And let's not forget that the wide variety of visualization customization options makes this Python library an indispensable tool for your data science toolbox.

Now, DataCamp has created a Bokeh cheat sheet for those who have already taken the course and that still want a handy one-page reference or for those who need an extra push to get started.

In short, you'll see that this cheat sheet not only presents you with the five steps that you can go through to make beautiful plots but will also introduce you to the basics of statistical charts. 

Python Bokeh Cheat Sheet

In no time, this Bokeh cheat sheet will make you familiar with how you can prepare your data, create a new plot, add renderers for your data with custom visualizations, output your plot and save or show it. And the creation of basic statistical charts will hold no secrets for you any longer. 

Boost your Python data visualizations now with the help of Bokeh! :)

Plotting With Bokeh

The Python interactive visualization library Bokeh enables high-performance visual presentation of large datasets in modern web browsers.

Bokeh's mid-level general-purpose bokeh. plotting interface is centered around two main components: data and glyphs.

The basic steps to creating plots with the bokeh. plotting interface are:

  1. Prepare some data (Python lists, NumPy arrays, Pandas DataFrames and other sequences of values)
  2. Create a new plot
  3. Add renderers for your data, with visual customizations
  4. Specify where to generate the output
  5. Show or save the results
>>> from bokeh.plotting import figure
>>> from import output_file, show
>>> x = [1, 2, 3, 4, 5] #Step 1
>>> y = [6, 7, 2, 4, 5]
>>> p = figure(title="simple line example", #Step 2
>>> p.line(x, y, legend="Temp.", line_width=2) #Step 3
>>> output_file("lines.html") #Step 4
>>> show(p) #Step 5

1. Data 

Under the hood, your data is converted to Column Data Sources. You can also do this manually:

>>> import numpy as np
>>> import pandas as pd
>>> df = pd.OataFrame(np.array([[33.9,4,65, 'US'], [32.4, 4, 66, 'Asia'], [21.4, 4, 109, 'Europe']]),
                     columns= ['mpg', 'cyl',   'hp',   'origin'],
                      index=['Toyota', 'Fiat', 'Volvo'])

>>> from bokeh.models import ColumnOataSource
>>> cds_df = ColumnOataSource(df)

2. Plotting 

>>> from bokeh.plotting import figure
>>>p1= figure(plot_width=300, tools='pan,box_zoom')
>>> p2 = figure(plot_width=300, plot_height=300,
x_range=(0, 8), y_range=(0, 8))
>>> p3 = figure()

3. Renderers & Visual Customizations 


Scatter Markers 
Bokeh Scatter Markers

>>>[1,2,3]), np.array([3,2,1]), fill_color='white')
>>> p2.square(np.array([1.5,3.5,5.5]), [1,4,3],
color='blue', size=1)

Line Glyphs 

Bokeh Line Glyphs

>>> pl.line([1,2,3,4], [3,4,5,6], line_width=2)
>>> p2.multi_line(pd.DataFrame([[1,2,3],[5,6,7]]),

Customized Glyphs

Selection and Non-Selection Glyphs 

Selection Glyphs

>>> p = figure(tools='box_select')
>>> p. circle ('mpg', 'cyl', source=cds_df,

Hover Glyphs

Hover Glyphs

>>> from bokeh.models import HoverTool
>>>hover= HoverTool(tooltips=None, mode='vline')
>>> p3.add_tools(hover)

Color Mapping 

Bokeh Colormapping Glyphs

>>> from bokeh.models import CategoricalColorMapper
>>> color_mapper = CategoricalColorMapper(
             factors= ['US', 'Asia', 'Europe'],
             palette= ['blue', 'red', 'green'])
>>>  p3. circle ('mpg', 'cyl', source=cds_df,
                 transform=color_mapper), legend='Origin')

4. Output & Export 


>>> from import output_notebook, show
>>> output_notebook()


Standalone HTML 

>>> from bokeh.embed import file_html
>>> from bokeh.resources import CON
>>> html = file_html(p, CON, "my_plot")

>>> from  import  output_file,  show
>>> output_file('my_bar_chart.html',  mode='cdn')


>>> from bokeh.embed import components
>>> script, div= components(p)


>>> from import export_png
>>> export_png(p, filename="plot.png")


>>> from import export_svgs
>>> p. output_backend = "svg"
>>> export_svgs(p,filename="plot.svg")

Legend Location 

Inside Plot Area 

>>> p.legend.location = 'bottom left'

Outside Plot Area 

>>> from bokeh.models import Legend
>>> r1 = p2.asterisk(np.array([1,2,3]), np.array([3,2,1])
>>> r2 = p2.line([1,2,3,4], [3,4,5,6])
>>> legend = Legend(items=[("One" ,[p1, r1]),("Two",[r2])], location=(0, -30))
>>> p.add_layout(legend, 'right')

Legend Background & Border 

>>> p.legend. border_line_color = "navy"
>>> p.legend.background_fill_color = "white"

Legend Orientation 

>>> p.legend.orientation = "horizontal"
>>> p.legend.orientation = "vertical"

Rows & Columns Layout


>>> from bokeh.layouts import row
>>>layout= row(p1,p2,p3)


>>> from bokeh.layouts import columns
>>>layout= column(p1,p2,p3)

Nesting Rows & Columns 

>>>layout= row(column(p1,p2), p3)

Grid Layout 

>>> from bokeh.layouts import gridplot
>>> rowl = [p1,p2]
>>> row2 = [p3]
>>> layout = gridplot([[p1, p2],[p3]])

Tabbed Layout 

>>> from bokeh.models.widgets import Panel, Tabs
>>> tab1 = Panel(child=p1, title="tab1")
>>> tab2 = Panel(child=p2, title="tab2")
>>> layout = Tabs(tabs=[tab1, tab2])

Linked Plots

Linked Axes 

Linked Axes
>>> p2.x_range = p1.x_range
>>> p2.y_range = p1.y_range

Linked Brushing 

>>> p4 = figure(plot_width = 100, tools='box_select,lasso_select')
>>>'mpg', 'cyl' , source=cds_df)
>>> p5 = figure(plot_width = 200, tools='box_select,lasso_select')
>>>'mpg', 'hp', source=cds df)
>>>layout= row(p4,p5)

5. Show or Save Your Plots  

>>> show(p1)
>>> show(layout)
>>> save(p1)

Have this Cheat Sheet at your fingertips

Original article source at

#python #datavisualization #bokeh #cheatsheet

Simpliv LLC

Simpliv LLC


Mastering Tableau Step by Step | Simpliv

Tableau is a widely used data analytics tool. It is the most powerful, secure, end to end platform for your data. Designed for the individual but scaled for the enterprise. Tableau is the only data intelligence platform that turns your data into insights that drive action. Learn data visualization in an easy step by step manner that even a non-analyst can understand.

In this course, you will learn what you need to know to analyze and display data using Tableau Desktop - and make better, more data-driven decisions for your company.

Basic knowledge
Basic knowledge of Excel expected
What will you learn
Difference between Tableau and Excel
Data types in Tableau
Live v/s Extract Data
View Data
Measure Names and Values
Joining Tables
Splitting Columns
Introduction to Maps
Defining Groups
Defining other Groups
Editing Groups
Creating Dashboards
Editing Dashboards
Creating a new storyline

#Tableau Step by Step #Mastering Tableau Step by Step #dataandanalytics #Tableau

Lily Thomas

Lily Thomas


What Are The Benefits Of Using Sentiment Analysis on Instagram

These days, everyone and their furry mates (no jokes) have Instagram accounts. Truthfully, Instagram captions (the text that a user writes while posting a photo) are a great source to get textual data that can be mined and analyzed easily. 

Even though the importance of emotions in marketing is well known, still quantifying emotions is not as easy as measuring data metrics – likes, mentions, and comments. But, with sentiment analysis today, brands can translate those feelings into actionable business data with ease. 

Simply put, Instagram sentiment analysis is an ideal choice to discover and attract new target audiences and also improve brand presence. Data shows that the projected marketing spending on Instagram influencer-led marketing programs is predicted at $8.08 billion. 

Sentiment analysis improves your operational efficiency as it gives you a peek into the reason behind the good and bad sentiments in posts and comments. That’s why using an Instagram sentiment analysis, can help you gauge aspects like brand awareness, and brand image and predict consumer behavior.

Here in this blog, we will shed light on how to implement sentiment analysis on Instagram, and how it can benefit you. 

Which Formats are Used for Extracting Customer Insights From Instagram Social Listening?

The primary formats for getting sentiment analysis on Instagram includes posts, comments, hashtags, videos, and Instagram TV. Let’s find out. 

Post and Comments 

You can identify the JSON data from a specific URL account to discover insights like followers, account name, people followed, number of posts etc. Even, a web scraper tool is used for more in-depth data collection. 

Instagram’s official API offers access to Instagram data that is related to your account. So, to get customer insights from other accounts, you’ll need to work outside of Instagram’s API along with a web scraper. A web scraper automatically extracts data from the platform by sending HTTP requests to different web pages focusing on downloading them. Then, the data is parsed and saved to a database where the visualization software is used to make sense of the information. 

One approach of scraping Instagram posts and comments is through Python. A Python-based insight-gathering tool like Selenium is a hot pick of marketers. 


Hashtags are an excellent choice for extracting sentiment analysis on Instagram. With an Instagram Hashtag scraper like Instascrape, you can quickly vet 22 different data points surrounding a single Instagram hashtag to compile data related to those hashtags. 

This includes commonly associated words and accounts. Apify is an excellent Instagram scraper used to customize focus on hashtags added to profiles, comments, posts, and places. This includes several different automation tools in addition to a scraper. The data output format is in JSON.

Videos and IGTV

Instagram TV (IGTV) and reel are both equally beneficial. These scrapers provide different data types, like recent comments, full JSON data, etc. 

Instascrape is a popular video scraper for Instagram. This can scrape long-form videos on IGTV, story videos, post videos, reels, and more. The syntax remains simple and offers flexible Instagram data in general. 


What Are The Benefits of Using Sentiment Analysis On Instagram

Sentiment analysis on Instagram helps you keep track of brand positioning with customer insights. Also, it helps you avert a PR crisis and strengthen your branding efforts. 

Without any ado, let’s look at some of the major benefits: 

Stronger Brand Performance 

You would have thousands of Instagram posts that collect engagements but none to understand them. Instagram sentiment analysis provides all the required avenues needed to understand how your business account is doing. 

Better Audience 

Instagram sentiment analysis helps you understand the audience better. You can interpret user engagement, demographic behavior, and brand mentions, and find the nuances in emotions. 

Competitor Analysis 

With Instagram sentiment analysis you can track your competitors better. Watching how your audience engages with your competitors will provide you with perspectives that you wouldn’t have got otherwise. 

Focused Marketing

Undoubtedly, marketing and advertising campaigns benefit significantly from sentiment analysis of Insta comments and videos. Also, it helps you identify potential mistakes in marketing strategies. 

Groundbreaking Customer Service 

Instagram sentiment analysis helps you improve your customer service program and bridge the gaps if any. 

Improvised Product  

Sentiment analysis of Instagram gives insight into product enhancement including new features, optimal pricing, and many other aspects. 

Finally, How To Use Instagram Sentiment Analysis Solution?

Data collected from Instagram videos are edited and processed for text analytics to disambiguate entities. Further, they are processed for sentiment analysis presenting data on a dashboard. 

Below, are the steps describing how a sentiment analysis solution extracts data from this platform: 

#Step 1: Speech to Text Transcription 

All video files collected for sentiment analysis on Instagram are converted into text using a speech-to-text model. Besides, data can be gleaned using web scraping tools. Several open-source scrapers do this job. Some are:

  • Instalooter
  • Instagram Scraper
  • Instaloader
  • Instagram PHP Scraper
  • Socialmanagertools Igbot

#Step 2: Caption Overlay 

Videos are broken frame-by-frame into image formats. Any text that appears in these frames is identified and extracted. Once the scraper finds the caption, it can scrape the last ten or so captions from the respective page in one instance. 

The Instagram Graph API and Instagram Basic Display API are considerable choices for this purpose. 

#Step 3: Image Recognition 

Similar to captions, with video analysis AI, the model also recognizes logos and images present in the background. Brand names and logos are identified and classified as entities. 

Read further at: Benefits Of Using Sentiment Analysis on Instagram