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Mental health problems like depression and anxiety are difficult to identify in their early stages. According to the Stanford Natural Language Processing Research Group, in the US alone, 43.6 million adults (18.1%) experience mental illness each year. The longer the individual is without treatment, the worse is the impact on the health of the individual. Fortunately, mental health conditions can often be treated with counseling and psychotherapy, and in recent years there has been rapid growth in the availability of these treatments thanks to technology-mediated counseling. Adaptability: Successful counselors are aware of how the conversation is going and react accordingly. Dealing with ambiguity: Successful counselors clarify situations by writing more, reflecting back to check to understand, and making their conversation partner feel more comfortable through affirmation. Creativity: Successful counselors respond in a creative way, not using too generic or “templated” responses. Making Progress: Successful counselors are quicker to get to know the main issue and are faster to move on to collaboratively solving the problem. Change in Perspective: Researchers found that people in distress are more likely to be more positive, think about the future, and consider others when the counselors bring up these concepts. This kind of perspective change is associated with positive conversations, a finding that is consistent with psychological theories of depression. According to researchers, “Although some of these are obvious in hindsight, this is to the best of our knowledge the first time someone has been able to perform a large-scale analysis of these strategies. We hope that this research will lead to a better understanding of how to provide quality counseling services.”  

Key Findings

At the beginning of the conversation, the language used in positive and negative conversations is quite similar, but then the distance in a language increases over time. This increase in distance is much larger for more successful counselors than less successful ones, suggesting they are more aware of when conversations are going poorly and adapt their counseling more in an attempt to remedy the situation. Interestingly, although more successful counselors tend to more often use structured responses like check questions, their responses also tended to be more unique. Researchers measured the uniqueness of responses by clustering counselor messages and then counting how many close neighbors the messages tended to have. Messages from more successful counselors tended to have fewer neighbors, suggesting they were being more creative or personalized in their responses. This tailoring of messages requires more effort from the counselor, which is consistent with the results in the above table showing that more successful counselors put in more effort in composing longer messages as well. According to Stanford researchers, prior work on counseling suggests that certain perspectives are associated with depression, such as having a negative view of the future or being self-focused. They quantified the concept of perspective change by measuring the frequency of different word categories (provided by LIWC) over time in the conversation. Texters start explaining their issues largely in terms of the past and present, but over time switch to talking about the future. Additionally, texters writing more about the future are more likely to feel better after the conversation. This suggests that changing the perspective from issues in the past towards the future is associated with a higher likelihood of successfully working through the crisis. The Stanford researchers also investigated whether counselors could instigate this perspective change, and found that texters were more likely to talk about the future if the counselor brought up the subject.  

Conclusion of the Study

As NLP techniques become more effective and data becomes more available, it is becoming increasingly useful as a tool for investigating pressing issues that our societies face. The researchers think mental health is one such problem and they hope their research on counseling will inspire future work on the area, leading to new insights that could benefit treatments for mental illness. Such research could improve counselor training and lead to tools that help counselors be more successful.

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Fashion, Identity, And Mental Health

Fashion, Identity, and Mental Health

Abiola Agoro on the runway during 2023’s New York Fashion Week. Photo by Kapcherd  Photography


Fashion, Identity, and Mental Health Abiola Agoro (CAS’21) shares the story behind her upcoming fashion brand launch

Growing up outside of Fort Worth, Tex., Abiola Agoro has loved fashion since she was little. She was just nine, she says, when she started selling her own jewelry, which she made from polished stones she and her mother purchased at local mineral shows. They sold the jewelry from a store they rented next to their church. When she arrived at BU, Agoro’s interest expanded into fashion styling, and she launched her own mobile boutique and styling service, Styled by Ola, that offers personal styling services, customized jewelry, and clothing. Her work has been seen in numerous runway shows, including at New York Fashion Week. 

Agoro (CAS’21) is about to embark on an even bigger venture: launching a new online clothing line designed and made by her, to debut June 19. The line, called Ruthanne, is named for her maternal grandmother. 

“My middle name is Ruth Anne and my grandmother’s name was Annie Ruth. So the [brand name] is like an ode to my grandmother,” Agoro says. “She was just a badass woman, so I wanted to do a brand that honors her and honors the name.”

The fashion line features a variety of 1970s-inspired gender-neutral clothing, items like bell-bottom jeans, shirts, and jumpsuits, in addition to recycled and refurbished fur pieces. Prices range from $25 for accessories to $800 for garments like gowns. Ultimately, Agoro says, she wants her apparel to push the boundaries of what’s considered feminine and what’s considered masculine. For inspiration, she looks to West African culture—where men wear skirts casually—and the 1970s, when men and women both wore platform shoes.  

Models styled by Abiola Agoro with her design label Styled by Ola walk the runway during 2023’s New York Fashion Week. Agoro’s inspiration includes both West African patterns and sleek urban fashion.  Photo by Kapcherd Photography

“Some things are very much gender-oriented in West African culture, but men can wear things there that are considered extremely feminine here, and that’s not a problem,” Agoro says. “So I want to release clothing that does that, that questions [gendered boundaries].”

Agoro is Nigerian American: her father is from Nigeria and her mother is from LA. Both cultures inform her work. She also wants Ruthanne to be a brand that celebrates Blackness and invites others to join in that celebration. She grew up in rural Texas and spent much of her adolescence trying to blend in with the white spaces around her. Her brand, she said, counters that narrative of needing to assimilate. During high school, she began to embrace her skin color and the elements of Black culture she had been too timid to accept before. 

“I think what’s framed more of my philosophy now is making things that feel truthful to me in my Black experience, while also being able to pull on other people’s experience and give respect to those other experiences,” Agoro says. “And to acknowledge the vastness of who we are, because Black people are not monolithic.”

Before designing sketches for her new line, Agoro began with research. She’d seen styles that had been appropriated without credit from Black and African cultures and wanted to avoid making that same mistake. Instead, she wants her fashion lines to openly acknowledge Black and African cultures. 

“I think sometimes people hear something is a Black brand, and think, okay, it has to be all Black people wearing everything, and only Black people can buy the clothes,” Agoro says. “No, what it is is making sure that Blackness can be centralized without being commodified, without being taken over, and still be accepted in a way that is beautiful while being on Black people too.” 

Agoro first tried making clothes (using drapes and safety pins) when she was a child. But it wasn’t until her junior year at BU that she became serious about designing and making clothes in addition to styling. She began using her savings to buy material and take online sewing classes.

While her venture eventually turned into something she loved, it started out as a way to help her deal with an anxiety disorder diagnosed sophomore year. She says that designing and sewing clothes gave her a new creative outlet.

“I think my art has always been kind of a war with my mental health,” Agoro says. “Sewing came to me at a time when I was in transition with my mental health, and I think it was a beautiful thing, because it’s taught me so much about myself.”

She began sketching designs, and eventually, started making clothes. Sometimes her efforts turned out beautifully, leaving her awestruck, she says, other times, the final outcome looked nothing like she had imagined. 

A peek into Abiola Agoro’s digital design sketches, which include both clothing and the accessories to be worn with them.

Agoro says she’s developed a new perspective on her ability to create art. She once believed she had to be in pain in order to create something that was genuine and moving, but realizes now that she can create from a sense of joy. 

“I’m just now getting a balance of it,” she says. “There are some times where I get frustrated and won’t sew for two weeks, just because I’m sad, and I can’t. But it’s definitely so much better, and I’m able to do art from both a happy and a sad place and use it as a vessel for both rather than only aligning it with one or the other.”

“Abi doesn’t look left or right,” says her mother, Kimberly Agoro. “She keeps fighting for who she’s supposed to fight for. She fights for people to be treated with respect, dignity, and love.”

Friend and classmate Daniel Akomolafe (CAS’23), a model and fellow stylist, says he’s enthusiastic about Agoro’s new clothing line. 

“I’m so excited for Ruthanne because I feel it’s something that is very dear to her, so all artwork that comes from a place of sincerity and passion has the ability to change the world,” Akomolafe says. “I’m really proud of her for trusting herself and her artistic vision.”

With only weeks to go until the release of her first line, Agoro admits to being more than a little nervous. She’s spent the past year hand-stitching and sewing each piece, and she’s continuing to work on pieces for the launch. The line has been challenging, yet one she’s especially excited about. She wants it to be more than just another fashion launch, and hopes it’s something deeper for everyone who browses its contents. 

“I don’t want to just release a line,” Agoro says. “I want to release a story. I want to release a narrative.”

Explore Related Topics:

Role Of Yoga In Improving Mental Health And Well

Yoga is a practice that has been practiced for ages and has been found to improve both physical and mental health. Many people have found success with yoga as a means of coping with the psychological impacts of a fast-paced lifestyle. Previous studies have shown that yoga causes physiological changes that reduce stress and improve well-being.

Psychological Well-being and Yoga

In general, two viewpoints have been used to define well-being. While the psychological perspective views well-being as the predominance of good characteristics, the clinical perspective views it as the absence of unfavorable conditions. Most of the six general criteria are typically included in positive psychological definitions of well-being. The well-known six-factor model, which incorporates the personal viewpoints of Erikson (1959), Maslow (1968), Rogers (1961), Allport (1961), and Jahoda (1958) into its six characteristics of well-being, has sparked a great deal of research on each of the numerous dimensions and how they individually contribute to psychological well-being. These six qualities of well-being are self-acceptance, positive relationships with others, autonomy, environmental mastery, and purpose in life. Several of Ryff’s six dimensions of well-being, such as self-acceptance, autonomy, and life purpose, have been linked to yoga in previous studies. Although recent studies by Callander (2013) imply that yoga rituals improve pleasant ties with others by promoting prosocial behavior, research on yoga’s effects on relationships with others has been more limited. Physical health, lifespan, and life satisfaction are just a few outcomes of which psychological well-being is a significant predictor. Psychological benefits of regular yoga practice

Stress reduction

Increased self-awareness

Less anxiety and depression

Improved concentration

Inner peace and calm

a more positive view of self/others

Increased body awareness and acceptance

Increased energy and vitality

Heightened sense of control of one’s body and mind

The decline in self-destructive patterns

Improved self-confidence

Increased mental clarity

Improved reaction time

Improved learning ability and memory

Increased ability to be present at the moment

Greater creativity

Improved sleep

Increased emotional stability

The techniques used in yoga treatment can vary depending on the problem. Here is a quick explanation of how yoga can treat several psychiatric conditions.

Depression − Fast practices like sukshma vyayama, fast surya namaskara, fast breathing exercises, and back bending positions should receive special attention. It is important to exercise caution when teaching meditation and relaxation practices for periods longer than 10 minutes.

Anxiety disorders/headaches − Particular attention should be paid to slow activities, including meditation, breathing exercises, relaxation techniques, and cooling pranayamas. Meditation and relaxation exercises should be practiced for longer periods.

Psychosis − Particular emphasis should be placed on alternating fast and slow yogic practices. Eyes should be open when performing asanas, and caution should be taken not to teach meditation to these patients.

Effect of Asanas

Effect of Yoga on Stress Hormonal Effects of Yoga

Melatonin, which may function as a hormone with psycho-sensitive properties, is known to be secreted more easily from the pineal gland as a result of yoga activities. It has helped improve healthy, average subjects’ well-being and functional capabilities. Such yogic techniques can certainly boost a patient’s functional aerobic capacity, especially if they cannot conduct weight-bearing aerobic exercise due to various musculoskeletal issues. The patients would be encouraged to follow these habits through their enhanced well-being.

Biochemical Effects of Yoga Scientific Evidence Related to Yoga in Psychiatric Disorders

According to research, practicing yoga backbends causes a rise in a good mood and a fall in a bad mood. Individuals may benefit from practicing back-bending poses to help them deal with depressive symptoms. They discovered notable decreases in despair, rage, anxiety, and neurotic symptoms in the 17 participants. Out of the participants, eleven experienced remissions. They found that practicing Sahaj Yoga helped depressed patients’ verbal working memory, attention span, visual-motor speed, and executive functions. The yoga group had much less psychopathology than the physical exercise group at the end of four months, according to research on individuals with schizophrenia.

Additionally, they considerably improved their quality of life and social and professional functioning. Yoga has been demonstrated to impact GABA and other brain chemicals significantly. The findings imply that yoga should be investigated as a potential treatment for depression and anxiety disorders characterized by low GABA levels. Yoga has been proven effective in treating a range of physical and psychological illnesses, including those caused by stress.

Yogic Principles of Healthy Living

The environment, surroundings, family, culture, eating habits, education, financial situation, and daily activities all influence someone’s lifestyle. When a person is fully content, has the capacity for growth, enjoys life, and develops harmony among all the layers of life (annamaya kosha, pranamaya kosha, manomaya kosha, vijnanmaya kosha, and anandmaya kosha), they are said to be in good health. Healthy living refers to how one works, plays enjoys, and spends their time in a more productive, joyful, and positive way or how one responds to life’s circumstances in a way that does not interfere with their regular activities. Nearly the same themes are emphasized in all writings discussing the yogic principles for the good life, and the goal determines the yoga practice. These tenets are frequently regarded as the foundational elements of yoga and are still relevant today. The yogic guidelines for a healthy lifestyle have been divided into

Ahara (Food) − It has to do with how people eat. The word “mitahara” in yogic practice, which denotes the perfect quality, quantity, and mental preparation, is used to describe the ideal diet. There are three types of foods that we eat, which are described below, according to Yogic scriptures

Sattavic − This food digests quickly, and this is easy to digest, naturally grown, and energizing. Milk, milk products like curd and cheese, fruits, dried fruits, seasonal vegetables, cereals, sprouts, legumes, honey, jaggery, sugarcane, natural and unprocessed sugar, and oil are among the sattavic foods.

Rajasic − The food is difficult to digest yet necessary for physically engaged people.

Tamasic − These stimulate the nervous system. Typically, they are not in a natural state. This group includes stale, frozen, canned, and bakery goods made with refined flour, like cakes and pastries, chocolates, soft drinks, tea, coffee, wines and liquor, cigarettes, etc. This kind of meal throws off the nerves’ natural equilibrium.

Vihara (Recreation) − It alludes to enjoyable, unwinding, and creative pursuits. These are the leisure pursuits one engages in during the free time. This healthy living approach gives such activities a great deal of importance. Painting, dancing, making pottery, sketching, singing, gardening, and playing are a few examples. The activity needs to be based on the person’s interests. Only then can someone truly enjoy it? A person should be encouraged to engage in creative hobbies like singing, painting, or any other activity that will aid in long-term mood regulation and management. A person has to develop the skill of mindful relaxation and self-discipline. Sleep is also included in the relaxation. Conscious relaxation is sleep. A peaceful, tranquil mind is necessary for sound sleep.

Achara (Conduct) − These consist of appropriate routines, attitudes, and conduct toward oneself and others. The yogic lifestyle demands that a person form healthy routines and practice self-control and discipline. It has been observed that exterior problems are frequently brought on by unsuitable life choices and can only be resolved by a person’s moral behavior. The methodical practices of Yama and niyama can help one behave better on a personal and societal level. According to this theory, a person must practice self- and societal discipline to live a happy life. A person who follows this process becomes organized. People may live happy lives if they have internalized these two aspects of Ashtanga yoga.

Vichara (Thinking) − The value of thinking is emphasized in this idea. Positive thinking’s benefits have been heavily emphasized in the media. According to yogic practices, having a positive outlook on life is crucial to finding happiness. Our thoughts have enormous power. According to the law of attraction, everything occurring to us now results from prior thoughts, whether conscious or subconscious.

Moreover, the thoughts we have right now will determine how things turn out. Everything that occurs to us, whether good or terrible, results from our mental attitude.

Vyavahara (Behaviour) − Our behavior toward others is called vyayahara. Yoga’s different books include instructions on how to interact with people. The Bhagavad Gita’s Karma Yoga and Maharishi Patanjali’s Kriyayoga are both highly pertinent. The Bhagavad Gita asserts that deeds must be carried out with a sequence of obligation and detachment. Tapa and swadhyaya are stressed by Patanjali, likewise, ishwarpranidhana. When performing daily tasks, there should be consistency and objectivity.


The Technology Behind Shrinky Dinks Can Make Better Robots

Imagine a robotic arm that doesn’t need mechanisms to move, or surgical tools that can expand or reshape themselves inside the body. While they may sound weird and wondrous, they actually already exist.

These wacky instruments all rely on shape-memory polymers, a category of materials that can shapeshift on its own. Now, researchers at Stanford University have made a shape memory polymer that’s stronger and more capable than any of its counterparts that came before it. They published their work last week in the journal ACS Central Science.

“[Shape-memory polymers] have been commercialized in our lives for a while,” says Shayla Nikzad, a graduate student at Stanford University and one of the paper’s lead authors.

A polymer is a very long molecule, built from many smaller molecules linked together in a giant chain. It’s no exaggeration to think that polymers run our world. The DNA behind the scenes of your cells is a polymer; silk and gelatin are also made of polymers. In your everyday life, you’re likely to happen across human-made polymers that make up plastic and synthetic rubber.

Shape-memory polymers are a very special type of polymer. When you bend them, the molecules in the polymer form some special bonds. And when you expose them to some stimulus—light, electricity, or, most often, a shift in temperature—those bonds will break and snap the polymer back into its original state.

In other words, shape memory polymers “remember” their original shape, and can reshape themselves back into that true form. Many engineers find that ability irresistible.

“So we can use shape-memory polymers to create a device … that changes shape without having to actually be pulled or stretched,” says Kai James, a professor of aerospace engineering at the University of Illinois Urbana-Champaign, who was not an author on the paper.

If you’ve ever played with a Shrinky Dink, you’ve seen a shape memory polymer in action. When you put one in the oven, it’ll shrink and harden. Of course, you can’t easily undo a Shrinky Dink after that; indeed, many shape memory polymers only “snap back” in one direction.

But shape-memory polymers are quite a bit more than just toys. In many ways, they’re already shaping the world around us. Some wires have a shape-memory coating that can shrink to give the wire better insulation. Shape-memory polymers in fabric can make it more breathable when hot and more waterproof when cold.

[Related: We finally have a working supersolid. Here’s why that matters.]

Researchers are also working to deploy shape-memory polymers in the hospital, for things like self-stitching sutures and stents that can fit into arteries and then expand, helping to increase blood flow.

Nikzad and her colleagues are especially interested in putting shape-memory polymers to work in robots. By creating artificial muscles from them, they could make robotic arms that don’t need heavy mechanisms like machinery or actuators in order to move.

“But one of the major roadblocks,” says Nikzad, “is just the fact that the materials essentially are not very strong.” 

Engineers could use shape-memory polymers to build a robotic arm that’s lighter and more flexible than its mechanical counterparts, but it wouldn’t carry as much without fracturing and falling apart. And shape memory polymers returning to their original state don’t provide very much force that can move or lift objects.

It’s possible to make stronger shape-memory polymers, but there’s a tradeoff: They can only act over lengths that are too short for most robots. Picture a high-technology version of  Tyrannosaurus rex’s tiny arms. 

The Stanford group wanted to see if they could do something about this conundrum. Working with chemists such as Stanford graduate student Christopher Cooper, the paper’s other first author, they took an existing polymer called PPG—today used to finish leather and to make paintballs—and modified its atomic structure to help its atoms form better bonds, thus creating a brand- new molecule. 

“That’s your run-of-the-mill organic chemistry,” says Nikazad.

When they tested it, they found that they’d created a new material that was capable of storing six times as much energy as any previous shape memory polymer. A plastic muscle made from this material can lift objects 5,000 times its own weight—and it’s not prohibitively expensive: The raw materials cost about $11 per pound.

“What these researchers have been able to do is overcome the tradeoff,” says James.

The researchers want to continue to work on their materials. For instance, their polymer’s shape-memory effect only works in one direction. That’s something they think they can improve upon. “For example, you could make something that heats up and it stretches, and then cools down and shrinks,” says Nikzad. This process repeats no matter how many times you heat or cool it. 

That, certainly, could create stronger, more lightweight robots. Beyond that, according to James, with the wide variety of applications that shape-memory polymers already have, the Stanford group’s new materials could be used in a good deal more than just robots.

How Professional Development Helps Businesses

Editor’s note: Looking for the right PEO to help you offer professional development and other employee programs? Fill out the below questionnaire to have our vendor partners contact you about your needs. 

What are some popular professional development benefits?

Professional development opportunities include online learning, workplace-hosted events, offsite seminars and workshops, and memberships to professional organizations. Professional development can also include employer support for schooling costs.

Access to new skill sets

“Today’s employees are unmistakably anxious to learn and get new skills, and the appropriation of innovation to empower employees’ learning enables associations to lift worker bliss while enhancing their capacity to hold ability,” said Alley Jones, technical writer for SysTools.

Many employers also offer access to online learning platforms, such as Lynda or Degreed. These platforms allow employees to guide their learning with preset pathways while also allowing managers to create pathways to help employees grow in their organizational roles. They typically include reward or gamification opportunities to incentivize learning.

According to the 2023 Employee Benefits Report from the Society for Human Resource Management (SHRM), the most common types of educational and professional development opportunities employers offer include professional organization memberships, offsite events, and workplace training or courses.

How does professional development benefit employers?

There are three significant ways professional development opportunities benefit employers. They help employers recruit new employees and retain their existing employees, and they help employees cultivate skills that will be used for the company’s benefit.

1. Education programs are a recruitment perk.

Aaron Filous, CEO of Promotable, said that in an environment where employees move from job to job quickly, professional development opportunities are an attractive draw for new talent.

“Whether an employee stays for decades or not, offering continuing education is still worth it,” Filous said. “It is a nice perk for recruiting that shows the company cares about the employee’s growth, and even if the employee is only there for a couple of years, it’s better to have more highly skilled employees for the same price.”

In SHRM’s report, 48% of HR professionals cited training and education programs as the most effective recruiting tool at their disposal. The Better Buys survey found that 78% of respondents currently have access to professional development, while 92% believe access is important or very important. 

According to the survey results, employees with access to professional development opportunities are 15% more engaged in their jobs, which led to a 34% higher retention rate. This means those employees are more productive day to day and less likely to quit their positions, which saves employers an average turnover cost of six to nine months of an employee’s salary.

“Hiring is expensive and time-consuming,” Filous said. “It is often easier and cheaper to retain your own talent or hire from within. Training or upskilling employees opens an additional talent pool for the employer that they already had.”

Did You Know?

The primary four recruiting models are traditional recruiting, outsourced recruiting, light internal recruiting and heavy internal recruiting.

3. Professional development cultivates hard and soft skills.

Professional development is a clear benefit to employees who want to improve their skills and value in the marketplace. It can help them earn a promotion internally or continue pursuing their career goals elsewhere, as their marketability to employers increases. However, it is also a boon for employers, who reap the benefits of a more skilled, satisfied workforce and an attractive tool for drawing in new, intrinsically motivated employees.

Professional development helps employees stay on top of new skills, especially in the tech field. Technology evolves at a lightning-fast pace, and it’s essential to train staff on the latest programs and tools. Employer-sponsored professional development opportunities are the definition of a win-win.


To identify a leader in your organization, observe their character and work ethic, test them out in a small leadership role, and get recommendations from co-workers.

Stretch assignments

Stretch assignments are a hands-on career development tool that can challenge employees, push managers to work closely with their teams and ensure your business moves in the right direction. 

It’s easy for workers to lose focus when they grow stagnant and perform the same duties day in and day out. When given a chance to “stretch” their abilities, employees can learn and grow while showing management they have the initiative and ability to contribute more meaningfully to the organization. 

The downside to stretch assignments is that they are energy-intensive for both the employee and the manager mentoring the project. To set up the employee for success, it’s essential to ensure the stretch assignment aligns with the employee’s skill set and that you give them enough time to plan and complete the project. It’s also helpful to give the employee access to resources and team support. 

Adam Uzialko contributed to the writing and reporting in this article. Source interviews were conducted for a previous version of this article.

Machine Learning Techniques For Text Representation In Nlp

This article was published as a part of the Data Science Blogathon.


Natural Language Processing is a branch of artificial intelligence that deals with human language to make a system able to understand and respond to language. Data being the most important part of any data science project should always be represented in a way that helps easy understanding and modeling, especially when it comes to NLP machine learning. It is said that when we provide very good features to bad models and bad features to well-optimized models then bad models will perform far better than an optimized model. So in this article, we will study how features from text data can be extracted, and used in our NLP machine learning modeling process and why feature extraction from text is a bit difficult compared to other types of data.

Table of Contents

Brief Introduction on Text Representation

Why Feature Extraction from text is difficult?

Common Terms you should know

Techniques for Feature Extraction from text data

One-Hot Encoding

Bag of words Technique



End Notes

Introduction to Text Representation

The first question arises is what is Feature Extraction from the text?  Feature Extraction is a general term that is also known as a text representation of text vectorization which is a process of converting text into numbers. we call vectorization because when text is converted in numbers it is in vector form.

Now the second question would be Why do we need feature extraction? So we know that machines can only understand numbers and to make machines able to identify language we need to convert it into numeric form.

Why Feature extraction from textual data is difficult? 

If you ask any NLP practitioner or experienced data scientist then the answer will be yes that handling textual data is difficult? Now first let us compare text feature extraction with feature extraction in other types of data. So In an image dataset suppose digit recognition is where you have images of digits and the task is to predict the digit so in this image feature extraction is easy because images are already present in form of numbers(Pixels). If we talk about audio features, suppose emotion prediction from speech recognition so in this we have data in form of waveform signals where features can be extracted over some time Interval. But when I say I have a sentence and want to predict its sentiment How will you represent it in numbers? An image dataset, the speech dataset was the simple case but in a text data case, you have to think a little bit. In this article, we are going to study these techniques only.

Common Terms Used

These are common terms that we will use in further techniques so I want you to be familiar with these four basic terms

Corpus(C) ~ The total number of combinations of words in the whole dataset is known as Corpus. In simple words concatenating all the text records of the dataset forms a corpus.

Vocabulary(V) ~ a total number of distinct words which form your corpus is known as Vocabulary.

Document(D) ~ There are multiple records in a dataset so a single record or review is referred to as a document.

Word(W) ~ Words that are used in a document are known as Word.

Techniques for Feature Extraction 1 One-Hot Encoding

Now to perform all the techniques using python let us get to Jupyter notebook and create a sample dataframe of some sentences.

import numpy as np import pandas as pd df = pd.DataFrame({"text":sentences, "output":[1,1,0]})

Now we can perform one-hot encoding using sklearn pre-built class as well as you can implement it using python. After implementation, each sentence will have a different shape 2-D array as shown in below sample image of one sentence.

1) Sparsity – You can see that only a single sentence creates a vector of n*m size where n is the length of sentence m is a number of unique words in a document and 80 percent of values in a vector is zero.

2) No fixed Size – Each document is of a different length which creates vectors of different sizes and cannot feed to the model.

3) Does not capture semantics – The core idea is we have to convert text into numbers by keeping in mind that the actual meaning of a sentence should be observed in numbers that are not seen in one-hot encoding.

2 Bag Of Words from sklearn.feature_extraction.text import CountVectorizer cv = CountVectorizer() bow = cv.fit_transform(df['text'])

Now to see the vocabulary and the vector it has created you can use the below code as shown in the below results image.


1) Simple and intuitive – Only a few lines of code are required to implement the technique.

2) Fix size vector – The problem which we saw in one-hot encoding where we are unable to feed data the data to machine learning model because each sentence forms a different size vector but here It ignores the new words and takes only words which are vocabulary so creates a vector of fix size.

2) Sparsity – when we have a large vocabulary, and the document contains a few repeated terms then it creates a sparse array.

3) Not considering ordering is an issue – It is difficult to estimate the semantics of the document.

3 N-Grams

The technique is similar to Bag of words. All the techniques till now we have read it is made up of a single word and we are not able to use them or utilize them for better understanding. So N-Gram technique solves this problem and constructs vocabulary with multiple words. When we built an N-gram technique we need to define like we want bigram, trigram, etc. So when you define N-gram and if it is not possible then it will throw an error. In our case, we cannot build after a 4 or 5-gram model. Let us try bigram and observe the outputs.

#Bigram model from sklearn.feature_extraction.text import CountVectorizer cv = CountVectorizer(ngram_range=[2,2]) bow = cv.fit_transform(df['text'])

You can try trigram with a range like [3,3] and try with N range so you get more clarification over the technique and try to transform a new document and observe how does it perform.


1) Able to capture semantic meaning of the sentence – As we use Bigram or trigram then it takes a sequence of sentences which makes it easy for finding the word relationship.

2) Intuitive and easy to implement – implementation of N-Gram is straightforward with a little bit of modification in Bag of words.

1) As we move from unigram to N-Gram then dimension of vector formation or vocabulary increases due to which it takes a little bit more time in computation and prediction

2) no solution for out of vocabulary terms – we do not have a way another than ignoring the new words in a new sentence.

4 TF-IDF (Term Frequency and Inverse Document Frequency)

Now the technique which we will study does not work in the same way as the above techniques. This technique gives different values(weightage) to each word in a document. The core idea of assigning weightage is the word that appears multiple time in a document but has a rare appearance in corpus then it is very important for that document so it gives more weightage to that word. This weightage is calculated by two terms known as TF and IDF. So for finding the weightage of any word we find TF and IDF and multiply both the terms.

Term Frequency(TF) – The number of occurrences of a word in a document divided by a total number of terms in a document is referred to as Term Frequency. For example, I have to find the Term frequency of people in the below sentence then it will be 1/5. It says how frequently a particular word occurs in a particular document.

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Inverse Document Frequency – Total number of documents in corpus divided by the total number of documents with term T in them and taking the log of a complete fraction is inverse document frequency. If we have a word that comes in all documents then the resultant output of the log is zero But in implementation sklearn uses a little bit different implementation because if it becomes zero then the contribution of the word is ignored so they add one in the resultant and because of which you can observe the values of TFIDF a bit high. If a word comes only a single time then IDF will be higher.

from sklearn.feature_extraction.text import TfidfVectorizer tfidf = TfidfVectorizer() tfidf.fit_transform(df['text']).toarray()

So one term keeps track of how frequently the term occurs while the other keeps track of how rarely the term occurs.

End Notes

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