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Data Science vs. Deep Learning vs. Machine Learning vs. Artificial Intelligence

In the modern world, using data as the fuel, Data Science drives different technologies toward automation.

For analyzing and visualizing a huge amount of data, we use various statistical methods.

When a user visits the website, Flipkart’s recommendation system marks and records the behavior and the movements of the visitor.

Also, whenever the user clicks on a product, the system stores it, along with other user activities, in the database.

Then, it uses collaborative filtering for suggesting products to customers who are likely to churn out for specific products.

Further, it also gathers and uses the data of ratings provided by buyers for a particular product.

Then, the recommendation engine combines and analyzes all this data with the help of tools available in Data Science.

It uses statistical analysis for visualizing and understanding the behavior of data in a neat and clean manner.

By this example, we can infer how crucial the data is and how the tools of Data Science pave way for the growth of any business.

Artificial Intelligence is the imposition of humans intelligence to robots or machines, especially computer systems.

It involves a three-step process: Artificial Intelligence consists of various frameworks that can be employed for computations in deep neural networks.

Strong Artificial Intelligence: In strong AI, the algorithms and instructions for a machine are designed such that they give the machine the ability to learn by itself from the given inputs and iteratively enhance accuracy by experience.

these algorithms inject abilities into a machine for performing distinct tasks without being denotatively programmed.

Before diving into the types of Machine Learning, if we talk about Machine Learning vs statistics, we would agree with the fact that visualization is not possible without performing accurate statistical analyses.

With the help of statistics, we can draw useful insights from data and build effective Machine Learning models on top of it.

Example: Weather forecasting is done on the basis of some labeled factors such as humidity, wind, temperature, atmospheric pressure, and precipitation.

It altogether gives a numeric value that predicts the weather condition, based on the above-mentioned factors.

Every time the player gets stuck in a dead-end, −10 points (punishment) are given.

Finally, after receiving many punishments and rewards, the player finds the right path to escape.

The layered architecture (deep neural network) in Deep Learning is inspired by the human biological neural network.

The outcome of this augmentation continues to move to the subsequent layer and acts as the input for that layer.

After that, at the output layer, the neural network yields an actual value for the regression task.

Finally, when the corresponding values of the weights give the output near to the actual, the neural network is fully trained.

Interestingly, a fully-trained neural network is capable of identifying an entity with greater efficiency in comparison with the regular neural network.

When we type a certain text or a word in Google, it recommends the relevant searches according to the keyword.

Data Analyst vs. Data Scientist - What's the Difference?

There are many – often quite different – opinions about the roles and skillsets that drive this thriving field, which creates much confusion.

So, before we attempt to understand the difference between a data analyst and a data scientist, let’s first take a historical look at the analytics business and each role in that context.

A data scientist is an expert in statistics, data science, Big Data, R programming, Python, and SAS, and a career as a data scientist promises plenty of opportunity and high-paying salaries.

Some of the data-related tasks that a data scientist might tackle on a day-to-day basis include: Businesses saw the availability of such large volumes of data as a source of competitive advantage.

It was clear that companies that could utilize this data effectively could make better business inferences and act accordingly, putting them ahead of competitors that didn’t have these insights.

To make sense out of the massive amounts of data, the need arose for professionals with a new skill set – a profile that included business acumen, customer/user insights, analytics skills, statistical skills, programming skills, machine learning skills, data visualization, and more.

This led to the emergence of data scientist jobs – people who combine sound business understanding, data handling, programming, and data visualization skills to drive better business results.

And in most cases, a data scientist needs to create these insights from chaos, which involves structuring the data in the right manner, mining it, making relevant assumptions, building correlation models, proving causality, and searching the data for signs of anything that can deliver business impact throughout.

Sentiment Analysis

Sentiment analysis is the automated process of classifying online text data as positive, neutral or negative, giving businesses the opportunity to gain a deeper understanding of how customers perceive their product, brand or service.

Whether you're a developer, a marketer, a data analyst, or you’re just interested in sentiment analysis, read this comprehensive guide to find out what sentiment analysis is, how it works, and where to start with sentiment analysis.

Usually, besides identifying the opinion, these systems extract attributes of the expression e.g.: Currently, sentiment analysis is a topic of great interest and development since it has many practical applications.

Companies use sentiment analysis to automatically analyze survey responses, product reviews, social media comments, and the like to get valuable insights about their brands, product, and services.

For example, one of our customers used sentiment analysis to automatically analyze 4,000+ reviews and better understand how their customers perceived their product.

Sentiment analysis can be applied at different levels of scope: There are many types and flavors of sentiment analysis and SA tools range from systems that focus on polarity (positive, negative, neutral) to systems that detect feelings and emotions (angry, happy, sad, etc) or identify intentions (e.g.

Sometimes you may be also interested in being more precise about the level of polarity of the opinion, so instead of just talking about positive, neutral, or negative opinions you could consider the following categories: This is usually referred to as fine-grained sentiment analysis.

Some systems also provide different flavors of polarity by identifying if the positive or negative sentiment is associated with a particular feeling, such as, anger, sadness, or worries (i.e.

Usually, when analyzing the sentiment in subjects, for example products, you might be interested in not only whether people are talking with a positive, neutral, or negative polarity about the product, but also which particular aspects or features of the product people talk about.

An alternative to that would be detecting language in texts automatically, then train a custom model for the language of your choice (if texts are not written in English), and finally, perform the analysis.

Sentiment analysis systems allows companies to make sense of this sea of unstructured text by automating business processes, getting actionable insights, and saving hours of manual data processing, in other words, by making teams more efficient.

Some of the advantages of sentiment analysis include the following: Can you imagine manually sorting through thousands of tweets, customer support conversations, or customer reviews?

There are many methods and algorithms to implement sentiment analysis systems, which can be classified as: Usually, rule-based approaches define a set of rules in some kind of scripting language that identify subjectivity, polarity, or the subject of an opinion.

The sentiment analysis task is usually modeled as a classification problem where a classifier is fed with a text and returns the corresponding category, e.g.

The classification step usually involves a statistical model like Naïve Bayes, Logistic Regression, Support Vector Machines, or Neural Networks: There are many ways in which you can obtain performance metrics for evaluating a classifier and to understand how accurate a sentiment analysis model is.

What cross-validation does is splitting the training data into a certain number of training folds (with 75% of the training data) and a the same number of testing folds (with 25% of the training data), use the training folds to train the classifier, and test it against the testing folds to obtain performance metrics (see below).

If your testing set is always the same, you might be overfitting to that testing set, which means you might be adjusting your analysis to a given set of data so much that you might fail to analyze a different set.

Precision measures how many texts were predicted correctly as belonging to a given category out of all of the texts that were predicted (correctly and incorrectly) as belonging to the category.

This means there is a good deal of agreement (since alpha is greater than zero), but we believe it’s still far from great (e.g.: around 0.8, which is the minimum reliability threshold social scientists use in order to say data is reliable, see here).

Most of the work in sentiment analysis in recent years has been around developing more accurate sentiment classifiers by dealing with some of the main challenges and limitations in the field.

However, detecting irony or sarcasm takes a good deal of analysis of the context in which the texts are produced and, therefore, are really difficult to detect automatically.

The problem is there is no textual cue that will make a machine learn that negative sentiment since most often, yeah and sure belong to positive or neutral texts.

For example, if the old tools the second text talks about were considered useless in context, then the second text turns out to be pretty similar to the third text.

Chances are that sentiment analysis predictions will be wrong from time to time, but by using sentiment analysis you will get the opportunity to get it right about 70-80% of the times you submit your texts for classification.

For typical use cases, such as ticket routing, brand monitoring, and VoC analysis (see below), this means you will save a lot of time and money -which you are likely to be investing in in-house manual work nowadays,- save your teams some frustration, and increase your (or your company’s) productivity.

In this section, we’ll take a dive into real life use cases, applications, and examples of the impact of all this can have on businesses, cities, and society – sentiment analysis in the wild, if you will.

Specifically, we’ll examine the use of sentiment analysis in the following: On the fateful evening of April 9th, 2017, United Airlines forcibly removed a passenger from an overbooked flight.

One such video, posted to Facebook, was shared more than 87,000 times and viewed 6.8 million times by 6pm on Monday, just 24 hours later.

On Monday afternoon, they tweeted a statement from the CEO apologizing for “having to re-accommodate customers.” Cue public outrage –you can imagine the field day on Twitter.

In today’s day and age, brands of all shapes and sizes have meaningful interactions with customers, leads, and even competition on social networks like Facebook, Twitter, and Instagram.

Most marketing departments are already tuned into to online mentions as far as volume –they measure more chatter as more brand awareness.

Sentiment analysis is useful in social media monitoring because it helps you do all of the following: Example: Trump vs Clinton, according to Twitter Over the course of a few months during the 2016 US Presidential Elections, we collected and analyzed millions of tweets mentioning Clinton or Trump posted by users from around the world.

For example: From this simple, easy analysis, we found interesting insights: To sum up, more people were tweeting about Trump, and a higher percentage of the people tweeting about Trump were doing so more positively than were the people tweeting about Clinton.

Not only do brands have a wealth of information available on social media, but they also can look more broadly across the internet to see how people are talking about them online.

Instead of focusing on specific social media platforms such as Facebook and Twitter, we can target mentions in places like news, blogs, and forums –again, looking at not just the volume of mentions, but also the quality of those mentions.

Then, they created a series of follow-up spin-off videos: one showed the original actor smashing the violin, and in another one, they invited a real follower who had complained on Twitter to come in and rip the violin away.

Using sentiment analysis (and machine learning), you can automatically monitor all chatter around your brand and detect this type of potentially-explosive scenario while you still have time to defuse it.

NPS surveys ask a few simple questions – namely, Would you recommend this company, product, and/or service to a friend or family member?

The goal is to identify overall customer experience, and find ways to elevate all customers to “promoter” level, where they theoretically will buy more, stay longer, and refer other customers.

Sentiment analysis is useful in understanding Voice of Customer (VoC) because it helps you do all of the following: Example: McKinsey City Voices project In Brazil, federal public spending rose by 156% from 2007 to 2015 while people’s satisfaction with public services steadily decreased.

Unhappy with this counterproductive progress, the Urban-planning Department recruited McKinsey to help them work on a series of new projects that would focus first on user experience, or citizen journeys, when delivering services.

McKinsey developed a tool called City Voices, which conducts citizen (customer) surveys across more than 150 different metrics, and then runs sentiment analysis to help leaders understand how constituents live and what they need, in order to better inform public policy.

By using this tool, the Brazilian government was able to surface urgent needs –a safer bus system, for instance– and improve them first.

If even whole cities and countries, famous for their red tape and slow pace, are incorporating customer journeys and sentiment analysis into their decision making processes, then innovative companies better be far ahead.

Sentiment analysis is useful in customer support because it helps you do all of this: Example: Analyzing customer support interactions on Twitter Just for kicks, we decided to do some analysis on how the four biggest US phone carriers (AT&T, Verizon, Sprint, and T-Mobile) handled customer support interactions on Twitter.

We downloaded tens of thousands of tweets mentioning the companies (by name or by handle), and ran them through a MonkeyLearn sentiment model to categorize each tweet as positive, neutral, or negative.

We then used our new Insight Extractor, which reads all text as one unit, extracts the most relevant keywords, and returns the most relevant sentences including each keyword.

Here’s some insights: To sum up, this could imply that a more personal, engaging take on social media elicits more positive responses and higher customer satisfaction.

Whether you’re exploring a new market, anticipating future trends, or keeping an edge on the competition, sentiment analysis can make all the difference.

Sentiment analysis is useful in market research and analysis because it helps you: Examples: Hotel reviews on TripAdvisor Our team was curious about how people feel about hotels in several major cities around the world, so we scraped and analyzed more than one million reviews from TripAdvisor.

Here’s some insights: We used the keyword extraction module to analyze the actual content of the positive/negative reviews, and found a few more interesting insights: Let’s say that you recently heard about sentiment analysis and it sounded like magic: automatically understanding if a particular message is speaking good or bad about something.

1) Read the basics Before diving into the sentiment analysis literature and tutorials, make sure you understand the very basics of sentiment analysis: Later on, if you feel courageous, you can explore more advanced sentiment analysis literature.

good next step in your journey to learn more about sentiment analysis is to play and experiment with an online demo, a place where you can simply type a message and test the results of the analysis for different expressions.

There is a sentiment analysis tutorial for almost everyone: coders, non-coders, marketers, data analysts, support agents, salespeople, you name it.

Python is the most common programming language for tutorials about data analysis, machine learning, and NLP (including sentiment analysis) but R is quickly catching up, especially with tutorials that aim at data scientists and statisticians.

By analyzing the different messages from these State of the Union speeches, it’s possible to get a lot of interesting insights like how the sentiment has changed over time or what presidents tend to have a more negative or positive speech.

Then, he compares these tokens against a list of words with associated positive or negative sentiments (a sentiment lexicon) and creates some visualizations using the ggplot package.

It starts by showing how to properly set up our environment, including jupyter notebook, an application that allows rapid prototyping and sharing of data-related projects.

Now that you have the data, you will need to set up the second step of your zap to run the sentiment analysis with MonkeyLearn, an AI platform that allows you to analyze text with machine learning.

It provides a friendly user interface where you can create complete data analysis workflows, including loading your data, running machine learning models, and create visualizations.

During this step, you have to specify your MonkeyLearn API token, specify which sentiment analysis model you want to use (Module ID), and select the input attribute (this would be the text sent to MonkeyLearn to perform the sentiment analysis).

So far, we’ve read about the basics of sentiment analysis, we’ve had a first-hand experience with a sentiment analysis model using an online demo, and we’ve gotten our hands dirty by experimenting with a tutorial in our domain.

The following are the most frequently cited and read papers in the sentiment analysis community in general: Bing Liu is an eminence in the field and has written a sound book that’s super useful for those starting research on sentiment analysis.

Liu covers different aspects of sentiment analysis including applications, research, sentiment classification using supervised and unsupervised learning, sentence subjectivity, aspect-based sentiment analysis, and more.

Another good way to go deeper with sentiment analysis is mastering your knowledge and skills in natural language processing (NLP), the computer science field that focuses on understanding ‘human’ language.

By combining machine learning, computational linguistics, and computer science, NLP allows a machine to understand natural language including people's sentiments, evaluations, attitudes, and emotions from written language.

Broadly speaking, they can be classified into two different categories: Within open source libraries, there are programming languages such as Python or Java that are particularly well positioned since they have a strong data science community and, as a result, open source libraries for data science, including natural language processing.

Keras provides useful abstractions to work with multiple neural network types like recurrent neural networks (RNNs) and convolutional neural networks (CNNs) and easily stack layers of neurons.

Usually, companies need to spend a lot of time, money, and resources in the following: If you want to avoid these hassles or you don't know how to code, a great alternative is to use sentiment analysis tools which usually solve most of the problems mentioned above.

The following is a list of sentiment analysis tools worth taking a look: Sentiment analysis can be applied to countless aspects of business, from brand monitoring to product analytics, from customer service to market research.

By incorporating it into their existing systems and analytics, leading brands (not to mention entire cities) are able to work faster, with more accuracy, toward more useful ends.

Sentiment analysis has moved beyond merely an interesting, high-tech whim, and will soon become an indispensable tool for all companies of the modern age.

Ultimately, sentiment analysis enables us to glean new insights, better understand our customers, and empower our own teams more effectively so that they do better and more productive work.

Artificial Intelligence Vs Machine Learning Vs Data science Vs Deep learning

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