Learn and achieve knowledge regarding Artificial Intelligence (AI) and its components. In addition, the description of terms like; Machine Learning, Natural Language Processing (NLP), Sentiment Analysis, Emotion Detection, and many more are also covered in this blog.
AI and its components
John McCarty first coined the term Artificial Intelligence (AI) decades ago in 1956 at the Dartmouth Conference. He defined AI as “the science and engineering of making intelligent machines”.
In simple terms, AI is termed as a technique of getting machines to work and behave like humans.
In the past context, it has been able to accomplish this by developing machines and robots that are applicable in a wide range of fields and sectors including education, health, business analytics, robotics, marketing, etc.
AI has found its way into our regular lives as it has become so general that we do not realize we utilize it most of the time.
For example, have you ever wondered how the most powerful search engine Google can provide you such effective search outputs? Also, how your Facebook newsfeed provides you the content based on your interests. These all are possible with the assistance of AI.
AI encompasses components like robotics, machine learning, deep learning, neural networks, knowledge-based expert systems, natural language processing, image processing, computer vision, object detection, etc.
Moreover, there are three types of AI. At first, Artificial Narrow Intelligence (ANI), also called weak AI involves applying AI only to particular tasks. Alexa as it operates within a limited predefined range of functions and there is no genuine intelligence or no self-awareness despite being a sophisticated example of weak AI.
Some more examples based on ANI include; autopilot features of Tesla face verification in Apple products, optimal pathfinding through Google Maps, Sophia, built at Hanson Robotics, and so on.
Another one is Artificial General Intelligence (AGI), also called strong AI involving machines that possess the ability to perform any intellectual tasks that a human can, but they are not yet capable of reasoning and thinking like humans. Many experts and researchers are researching the possibility of the existence of strong AI. The tech tycoon Elon Musk quotes that “AI is a fundamental risk to the existence of human civilization”.
In addition, the famous personality Stephen Hawking warned people that “Strong AI would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, could not compete, and would be superseded.”
Artificial Super Intelligence (ASI) refers to the time frame when the capacity of computer machines will surpass humans.
Currently, it is seen as a hypothetical, imaginary, or conceptual state which is portrayed in films and science fiction books where machines will take over the globe. However, tech masterminds like Elon Musk believe that ASI will take over the globe by the year 2040.
In the real-world scenario at present, AI is used in almost all sectors. It
has covered almost all the possible domains in the market from composing
poems and sonnets to spotting an eight planet in the solar system which is about
2500 light-years away. In the finance sector, JP Morgan’s Chase’s Contract
Intelligence (COiN) platform uses AI, machine learning, and image recognition
software to analyze legal documents in a matter of seconds.
Similarly, in the health sector, International Business Machines (IBM) is a pioneer that has developed AI software, especially for medicines. Millions of healthcare
organizations use IBM AI (Watson) technology for medical diagnosis.
A social media platform i.e. Facebook uses AI for face verification and the concept of machine learning and deep learning in AI is used
to detect facial features and tag your friends. Moreover, the use of machine
learning, deep learning, and natural language processing help in identifying hate
speech, and terroristic languages in tweets and filtering out offensive content.
Recommendation system used in Google search, YouTube is also the
application of AI. A newly released Google virtual assistant called Google
Duplex can not only respond to calls and book appointments for you but also
adds a human touch. AI has exponentially grown in recent times. AI is
branching out every aspect of our lives.
If this is the scenario then AI might take
over our lives sooner by then we need to develop some sort of machine that
assists us from escaping our very own creation and identity.
Machine Learning and NLP
Machine Learning is a tool and technique that permits the machines to gain from models, examples, and experience, and that too without being explicitly programmed so rather than you writing the code what you do is feed the data to the generic algorithm and the algorithm or the machine will still logic based on the given data.
Machine Learning is a branch of AI that we can utilize to answer our questions with the help of data. It uses the data to detect patterns in a dataset and adjust program actions accordingly and focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.
Machine learning plays a vital role in our daily life as well. Some of the applications of machine learning are Self-Driving Cars, Google Maps, Product recommendations, and so on.
The steps involved in machine learning algorithms include;
➢ Step 1: Data Collection: This step involves the collection of all the relevant
data from various sources.
➢ Step 2: Data Wrangling: This step involves the process of cleaning and
converting the raw data into a format that allows convenient consumption.
➢ Step 3: Analyze Data: Afterwards cleaning and converting the raw data
into a specific format, the data is analyzed to select and filter the data
required to prepare the model. Because not all the data is required for a
model you must select certain features.
➢ Step 4: Train Algorithm: Now after selecting the features, the algorithm is
trained on the training datasets through which the algorithm understands
the pattern and the rules which govern the data.
➢ Step 5: Test Algorithm: Then the testing datasets determine the accuracy
of the model and after this our model gets ready.
➢ Step 6: Deployment: Lastly, if the accuracy and speed of the model are
acceptable then the model gets ready for deployment in the real system.
After the model is deployed based on its performance, the model is updated, upgraded, improved, and maintained. And in case there is any
fault in the performance of the model then the model is re-trained.
There are broadly three major types of machine learning and they are;
a) Supervised Learning: The simplest form of machine learning is supervised learning.
Mathematically, in supervised learning, we have both
input data and corresponding output variables.
Any speech recognition or
speech automated system on your mobile phone trains your voice and then
starts working based on the training data which is an application of supervised learning.
In addition, the biometric attendance system is also an
application of supervised learning.
b) Unsupervised Learning: In unsupervised learning, the given data is
unlabeled and unstructured so it becomes very complex to classify the
data into various categories.
Mathematically, in unsupervised learning, we
only have the input data and no corresponding output variables This learning
is used to cluster (i.e. identifying groups of similar records and labeling the records according to the group to which they belong) the input data into
classes based on the statistical properties.
Market basket analysis is
based on unsupervised learning which is a key technique used by large
retailers.
c) Reinforcement Learning: In reinforcement learning, an agent is put in an
environment and learns to behave with the environment forming certain
actions and observing the result which it gets from those actions.
Unlike
supervised learning, there is no expected output in reinforcement learning, an
agent decides what actions to take to perform a given task in the
absence of a training dataset, and it is bound to learn from its own
experience.
Game is one of the applications of Reinforcement Learning.
AlphaGo is the breakthrough in the reinforcement learning process.
Natural Language Processing (NLP)
In the present context, the world is filled with data generated not only
by people but also by phones, computers, and other devices.
Coming to the 21st
century, according to the industry estimates only 21 percent of the available data
is present in the structured form and around 79 percent of data exists in the
textual form which is highly unstructured in nature.
So, here the techniques like
text mining or text analysis and NLP are introduced. NLP refers to the method of
AI of communicating with an intelligent system using the natural language.
Some of the applications of NLP are illustrated below;
➢ Sentiment Analysis:- Be it a Twitter or Facebook sentiment analysis, it’s
been used generally.
➢ Implementation of Chatbot:- It may include some customer chat services in
various companies and may apply to the sites.
➢ Speech Recognition:- Siri, Google Assistant, and Cortana are based on the
process of NLP.
➢ Machine Translation:- Google Translate uses NLP to translate data from
one language to another that too in real-time.
➢ Spell Checking
➢ Information Extraction
➢ Emotion Analysis
➢ Keyword Searching
➢ Advertisement Matching: Basically, recommendation of ads based on your
history
There are majorly two components of NLP.
- Natural Language Understanding (NLU)
- Natural Language Generation (NLG)
NLU maps the
given input into natural language into useful representation and analyzes those
aspects of the language whereas NLG is the process of producing meaningful
sentences and phrases in the form of natural language.
NLU is usually more
complex than NLG as it takes a lot of time and things to usually understand a
particular language especially if you are not a human being.
The steps involved in NLP include;
➢ Tokenization: It is the first step of NLP. It is the process of fragmenting
strings into tokens which in turn are small structures or units that can be used
for tokenization.
➢ Stemming: It is the process of normalizing words into their base form or root
form. For example; for words like; operation, operative, operating,
operates, operations, operated, the root or base word operates.
➢ Lemmatization: It is somehow similar to stemming, as it also maps several
words into one base root but in this process, the output is a proper word.
For example, a lemmatizer should map gone, going, and went in to go.
➢ Parts of Speech (POS) Tags: Generally, the grammatical types of the
words are considered the POS tags.
➢ Named Entity Recognition (NER): It is the process of recognizing the
named entity such as the name of a person, location, organization name,
quantity, etc.
➢ Chunking: Finally, in the context of NLP, chunking is the process of
picking up the individual pieces of words or tokens and grouping them into
a bigger ones known as chunks. This provides meaningful information
from the given text.
Sentiment Analysis and Emotion Detection
Sentiment Analysis is simply using machine learning to teach computers
to extract the sentiments out of our texts.
In addition, it is the contextual mining of
the text which determines and extracts the subjective information in source
material and helps a business to understand the social sentiment of their brand,
product, or service while monitoring the online conversation.
Basically, the
interpretation, prediction, and classification of sentiments or emotions within the
provided text data using the technique of text analysis is sentiment analysis.
Some companies like; Twitter, IBM, and Intel are now using sentiment analysis-related techniques and technologies to identify employee concerns and, in
some cases, develop programs to help improve the likelihood that employees will
remain on the job.
For example; suppose in the huge and famous companies like
Amazon, the customer's review matters a lot and the reviews are also in huge
numbers so manually inspection of the feedback review or rating provided by the
customers is not possible so the sentiment analysis technique plays a vital role.
In
conclusion, it is an amazingly essential technology for the business community
as it allows for obtaining realistic reviews from customers in an unbiased way.
In our regular life, we experience various circumstances and build
up emotions about them.
Emotion is a strong feeling about a human’s circumstances
or connection with others. Emotions oversee our regular lives; they are a major
part of the human experience and unavoidably they influence our decision-making.
The essential feeling levels are of six sorts to be specific; Love, Joy,
Anger, Sadness, Fear, and Surprise.
In the present context, a tremendous
amount of data, especially textual data is generated. It has gotten difficult to
manually analyze all the data for a particular purpose.
Emotion Detection (ED) dependent on related keywords is easy to
utilize and straightforward technique. New research
directions have risen from automatic data analysis like automatic emotion
analysis.
Emotion analysis has attracted researchers’ attention due to its
applications in various fields. For instance, security agencies can track emails /messages/ blogs, etc., and detect suspicious exercises. The business
communities these days prefer to utilize emotional marketing. In emotional
marketing, they attempt to stimulate customers’ emotions to buy products or
services.
Emotion Detection is the technique for
distinguishing human emotion from both facial and verbal expressions.
As we
have seen, to identify emotion in text NLP methods, machine learning, and computational linguistics are utilized.
The emotions that
prompt people to pen down certain words at specific times are what text‐based
ED is concerned about.
Text-based ED research studies have been given little
consideration in contrast to other modes of ED. the rule construction, machine
learning (ML), and hybrid approaches as the general approaches to
distinguishing emotions from texts.
The rule‐based approach outlines major
grammatical and logical rules to follow to recognize emotions from
documents. The rule construction approach includes keyword recognition (KR)
and lexical affinity strategies.
Problem Statement
A lot of the client's experience is enthusiastic, and emotions are what drive
clients to bother. Finding out about how clients feel has become a need in business,
advising everything from product improvement to advertising and client services.
Fundamentally, organizations remain aware of the client's point of view as they settle
on urgent choices and make crucial decisions. An innovation that can recognize and
analyze human emotions through text data can have an extraordinary impact on the
public’s daily life.
Emotion Detection Technology is being utilized in improving and enhancing client
services, business, and marketing research, interviewing, and optimizing the
emotional impact of digital advertising.
The innovation can determine emotions, such
as anger, sadness, fear, joy, disgust, surprise, trust, etc.
Adopting emotion detection
innovation into your business measures is the way to take advantage of this point
of view. This thriving business sector is being fuelled by advances in artificial
intelligence, and big investment from organizations (across a range of industries)
keen to study consumer behavior.
The emotion detection and recognition innovation
market is blasting and detonating with the venture. The market is anticipated to develop
to $65 billion by 2023.
Advertising and marketing include evoking an emotional
response from consumers in the hopes that the product, service, or brand will
resonate deeply. If a marketer can get an accurate read on someone’s emotional
response, they can change their missions and accomplish better outcomes in client
procurement (the act of acquiring something) or maintenance.
Also, medical services are another industry that could profit from this innovation.
Since emotion identification software is AI-controlled, it might help decide when
patients need medication or to assist specialists with organizing who to see.
Another
is the automotive business. In the manufacturers’ mission to assemble brilliant
vehicles, it bodes well for them to utilize AI to help them in understanding human
emotions.
Emotion detection software in vehicles can upgrade the general client
experience while likewise improving vehicle wellbeing.
On the off chance that a
vehicle is sufficiently brilliant to distinguish the emotional state of a driver, it can
assist in preventing accidents by imparting signs to the driver to stop the vehicle or
apply breaks.
With these, we can presume that emotion detection and analysis
innovation could be significant for problem-solving.
Research on Emotion Detection
In the current context, it has become impossible to analyze the data for
particular purposes due to the generation of a huge amount of text data.
So, automatic data analysis like automatic emotion analysis or detection features has
emerged immensely.
Emotion Detection and Analysis is considered a branch
of sentiment analysis that aims at developing applications that can analyze and
detect emotions expressed by the users in the given text.
At present, most business personnel use such technology to flourish in their market. They try to
analyze customers’ reviews and emotions about their products and services.
The
emotion detection and recognition market size is estimated to grow from USD
6.72 Billion in 2016 to USD 36.07 Billion by 2021, at a Compound Annual Growth
Rate (CAGR) of 39.9%.
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Research Work done on Emotion Analysis
Text-Based Emotion Analysis
Poonam Arya and Shilpa Jain performed a text-based emotion analysis
algorithm to detect whether the input text is happy, sad, fearful, joyful, etc.
Keyword-based emotion detection technique was implemented where a text document
was provided as input and emotion were to be provided as output at the end of
the program.
Firstly, the text that needs to be processed was provided then
proper cleaning and tokenizing of the text was carried out. Then removal of
stop words was done. Identification of the emotion keyword using a simple
emotion detection algorithm was determined and lastly, the extraction of the
emotion present in the text was observed.
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Keyword Based Technique for Emotion Analysis | |
Research on Emotion Detection
Rule-based is a very simple, easy to understand, and quick approach for
sentiment analysis. Being emotion analysis, a branch of sentiment analysis
rule-based approach for emotion detection from the text can be a better
approach.
The rule-based approach encompasses a simple concept of NLP. It
involves operations like tokenizing, removing stop words, cleaning the text,
stemming, applying basic algorithms, and resulting in analysis.
The algorithm
approaches the input text and verifies the word that matches the emotion.
Then the algorithm clarifies the nature of the text whether the text is positive
or negative.
The research was done on Emotion Analysis
In the context of emotion analysis, the keyword-based technique is based on
certain predefined keywords like; angry, happy, sad, surprise, fear, and so on.
Since it involves determining words to search for in the text, this approach is
simple and easy to understand and implement.
In this technique, the text
document data is considered as input. The text data is converted into tokens
and using these tokens emotional words are recognized. Then emotional words
are identified, and the intensity of the emotional words is analyzed.
Afterward, the text is checked whether the words in the text are also present in the emotion
then the dominant emotion word is found and is considered as the required
result of the input text document.
AI algorithm (Rule or Keyword Based Approach)
Rule or Keyword-based emotion analysis is based on an algorithm with a
clearly defined description of an opinion or text to identify. It involves a basic NLP
approach.
In this approach, the task is to find the occurrences of the emotions
like; happy, sad, fear, joy, hate, surprise, and so on in a written text document.
Once the keyword is distinguished within the text document then the occurrence
of the emotion is counted, and dominant emotion is set as a result.
For example;
if the input text data read “the movie was good. I am happy to see that movie.
Also, my friends are happy”.
Here the sentence is emotionally labeled with the
keyword happy. So, the emotional analysis of this text document may conclude
to be a happy emotion which is a positive emotion.
The actions to be taken for emotion analysis in this application are prescribed
below;
➢ Converting given text to lower case
➢ Removing the punctuations
➢ Tokenization
➢ Ignoring stop words in the text
➢ Basic NLP Emotion Analysis Algorithm
➢ Counting the dominant occurrence of the emotional word
➢ Displaying the dominant emotion as the final output emotion.
In this NLP algorithm, the set of words and their respective emotions are
described in a file. And the algorithm is applied to find the emotional result for
those texts or paragraphs given by the users.
Firstly, the given text or paragraph
must be cleaned which means converting the text into lower case and eradicating
the punctuations. The splitting of the words from the text (i.e. tokenization) occurs
along with removing the stop words as stop words do not add any meaning to the
text or paragraph.
After this, the simple emotion analyzing NLP algorithm is implemented. Proper verifying as the words in the given text match with the list
of emotions is carried out through looping and in case the emotion is present
then the emotion gets added to the list finally, each emotion occurrence count
is carried out and displayed in the graph.
Lastly, the emotion that occurs must be
considered the dominant emotion, and the text or paragraph sentiment is
expected to be the reflection of that particular dominant emotion.
Solution addressing real-world problems
Companies across the globe are harnessing the power of emotional
intelligence to improve and flourish their business processes.
What we have
seen at present is just a snippet of the true market potential of emotion analytics.
With the rapidly evolving rate of data, manual analysis has become impossible
so sentiment analysis may help in analyzing and detecting the data using some
sort of sentiment analysis techniques.
The emotion Analysis technique helps to
improve an existing process, grab new golden opportunities and reduce
expenditure for any customer-related business. Making the interview process
bias-free, generating high engagement for digital advertisement, improving
customer experience and reviews, and so on related terms can be enhanced
using emotion detection methods.
Emotion analysis is a fabulously essential innovation for business since it
permits obtaining sensible and realistic reviews or feedback from the customers in
a fair way.
To sum up, the future of emotion detection in customer care and
support is promising and the technology will turn out to be more widely adopted
throughout the following not many years.
Regardless of whether it’s analyzing
text documents, videos, or speech, having the option to make significant
information with negligible effort continuously could alter the technique of business
approach to customer communications in the upcoming period.
How we
convey is not altering excessively, however how data is being gained from these
conversations is advancing rapidly.
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