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Text analytics is a major aspect of natural language processing and involves automatically extracting insights from massive amounts of unstructured text data.
Since text analytics leverages machine learning more than human labor, there are many applications for organizations across virtually every industry.
Text analytics is also commonly paired with data transcription tools for seamless work processes. First, the data transcription tool converts audio recordings from qualitative research into text transcripts. Then, the text analytics tool will process the dataset and highlight recurring topics or sentiments.
All that being said, studies show that only 18% of organizations are taking advantage of unstructured data which is significant since up to 90% of all data is unstructured. In other words, there is a huge opportunity for you to capitalize on this wealth of untapped data and stand apart from your competitors.
As powerful as text analytics can be, a worker is only as good as its tool, or specifically their mastery of the tool at hand.
If you want to effectively take advantage of text analysis, you must first understand its inner workings: what is text analytics, how it works, and how you can leverage text analytics for your organization.
Text analytics uses natural language processing (NLP) techniques to quickly analyze chunks of text data. These unstructured, semi-structured, and structured text data come in many forms.
Social media messages, marketing surveys, product reviews, and emails are all examples of useful text data.
Through text analytics, organizations can process and extract actionable insights from overwhelming amounts of text data.
This is important since text analytics is a consistent and efficient way to minimize errors and researcher bias.
The specific information to be extracted depends on your needs. Some examples of text analysis use cases include sorting spam emails, identifying prevalent topics, and monitoring brand reputation.
People often use the terms text mining and text analysis interchangeably, that’s because they both share the same meaning. Text mining and text analysis are concerned with extracting information from large volumes of text data and then converting this information into actionable insights.
In that sense, text analytics and text analysis both share the same goal of analyzing unstructured text data. However, there are slight differences between the two terms. Essentially, text analysis involves qualitative analysis, whereas text analytics involves quantitative results.
For example, text analytics of social media messages will gather all that unstructured data, and sort them into categories. The text analytics model may create a graph to visualize how frequently specific words occur and their seasonality trends.
Then, the manager will conduct text analysis and identify which social media messages resulted in positive or negative results, and what they can do about it.
Text analysis (or text analytics) models often combine text analytics and text analysis, making their differences insignificant. Thus, to avoid confusion, we’ll refer to text analytics and text analysis as the same thing.
What’s more important is understanding how text analytics models work, and how you can apply them to increase the bottom line of your organization.
Text mining utilizes natural language processing and machine learning techniques to extract insights from text data. While all three often overlap in the data science field, they all have different meanings and focuses.
Essentially, text analytics involves utilizing machines to process unstructured text data at scale. When processing the text data, the text analytics models will utilize NLP techniques to produce accurate results.
One such NLP technique is tagging the parts of speech of a sentence, which will be helpful for further analyses.
Organizations will also continuously train text mining algorithms by feeding large volumes of text. Through constant training and feeding of text data, the algorithm will improve its text analysis accuracy and keep up with the evolution of language.
The text analysis process utilizes a mixture of natural language processing (NLP) and machine learning methods. As such, you must have a background in NLP and machine learning to build an effective text analytics model.
There are a few types of text analytics models, including rule-based, machine-learning, and hybrid models. These approaches will affect the overall text analytics process and the level of human involvement.
The most common approach in text analytics and other NLP models is the rule-based approach. Before you even create a text analytics algorithm, you must first create a list of rules. In those lists (or datasets), you manually document the association between a word and a tag.
The text analytics algorithm will then process chunks of text and classify words according to those predetermined rules. How you categorize texts depends on your organization’s needs.
For instance, you can assign a spam tag to certain emojis or words in an email. Another text classification use case is to assign negative to words such as bad, terrible, and awful.
Rule-based models are simple and easier to create than machine learning models. Moreover, there is a collection of open-source datasets online that you can download and implement into your text analytics machine for free.
However, rule-based text analytics may produce inaccurate results when processing ambiguous sentences. For example, sentences that contain sarcasm, dialects, memes, and the message’s context. Furthermore, adding new rules to the algorithm is more difficult, making it more difficult to scale than machine learning alternatives.
In machine learning models, you train the algorithm by feeding it a copious amount of text data. These data are pre-tagged with the relevant classifiers.
The engineer must also ensure that the training data is accurate and bias-free. If not, the machine learning model will pick up these bad habits and result in inaccurate results.
Through continuous feeding of pre-tagged data, the machine learning model will be able to automatically predict and classify future input with pinpoint accuracy. As a result, you can scale machine learning text analysis easily and lead to economies of scale.
Machine learning models also utilize Naive Bayes algorithms(a probabilistic method) and deep learning to enhance their analysis accuracy. Thus, the more you train the machine learning model, the better it becomes in big data text mining.
However, the initial investment and continuous training of machine learning models can be resource-heavy. Not to mention the computing power required to run machine learning algorithms. Feeding inaccurate or biased datasets may also affect the text analysis’ results.
Hybrid text analytics models combine the best of both rule-based and machine learning models. By combining various rule-based and machine learning algorithms, the text analytics model can produce the most accurate results.
While hybrid models produce the most accurate results, they also incur the most upfront investment and maintenance costs.
Text analytics is a methodical process of gathering, processing, and presenting actionable insights from vast amounts of text data. While varying models approach this process differently, the general steps of text analysis remain the same:
Before the text analytics machine can analyze anything, it must first have an input of text data. These text data can be unstructured, semi-structured, or structured.
Unstructured text data refers to all the words that you can gather online that haven’t been organized into any labels. For example, social media comments, text messages, and entire documents. You can think of unstructured data as messy, ‘wild’ data that hasn’t been organized.
On the other hand, structured text data refers to texts that have been arranged into certain parameters. These data have already been labeled and are neatly stored in their respective folders. Common business examples of structured data include sales transactions, log-in details, and demographic information.
You can gather all these text data from internal and external sources. Internal sources refer to collecting data from databases within your organization and its systems. Conversely, external data sources come from anywhere outside your organization.
You can also utilize data collection APIs into your stack to speed up your work processes. APIs are basically integrations that you can program into other applications and allow you to collect text data from those applications.
Internal data refers to any data that you retrieve from within your organization. This includes any computer applications, documents, systems, and departments. Internal text data are a great starting point for data collection because of their immediate availability and cost-effectiveness.
You can gather internal data from your CRM software, emails, owned media analytics reports, knowledge management software, and from other departments in your organization. Scour through your organization for any documents (physical and digital), reports, survey feedback, and any other medium that you use to store text information
Internal sources of text data may contain undiscovered insights about your customer but are often hidden in silos. For example, your customer service team may have valuable amounts of customer feedback that you can use to conduct text analysis.
Pros of internal text data:
✅ Easily obtainable
✅ Less expensive
✅ More specific and relevant to your organization
Cons of internal text data:
❌ Smaller sample size
❌ May be outdated
External data refers to data that comes from anywhere outside of your organization. This includes social media, product reviews, user-generated content, open-source datasets, and other websites.
There is essentially an infinite amount of external text data available – whenever someone posts a comment on social media, external text data is created.
The biggest advantage of external data is its quantity. You can obtain large amounts of text data to train a text analytics model.
However, you must ensure that this data is accurate and comes from authoritative sources. If not, your text analysis will produce inaccurate results and in turn, misguided decisions.
You can also integrate data collection APIs into social media platforms such as Instagram, Twitter, and Facebook. The APIs will allow you to quickly extract text data such as comments, profile bios, and so on.
Pros of external text data:
✅ Vast amounts available
✅ Can compare historical data over time
✅ APIs available for easy collection
Cons of external text data:
❌ May be inaccurate and/or outdated
❌ More expensive and time-consuming
The text mining model cannot analyze unprocessed raw data as they are. Raw text data contains noise such as punctuations, stopwords, and characters in different cases.
For us, making sense of these elements is common sense, but a machine may not interpret the text sensibly. So to make the machine understand raw text data more easily, it must first process the data using various NLP techniques:
Tokenization is the process of breaking down raw text data into smaller units which we call tokens. It is also a crucial aspect of text preprocessing in text analytics and other NLP models.
Compartmentalizing entire documents of text into tokens makes it easier for the machine to analyze. It’s no different from how humans process text. For instance, it’s easier to digest this blog article by separating it into chapters, as compared to going through everything at once.
Depending on the task at hand, we can tokenize text by words (word tokenization) or by sentences (sentence tokenization). Here’s an example of what word tokenization looks like for “Tokenization is the process of breaking down raw text data into smaller units.”
[‘tokenization’, ‘is’, ‘the’, ‘process’, ‘of’, ‘breaking’, ‘down’, ‘raw’, ‘text’, ‘data’, ‘into’, ‘smaller’, ‘units’]
The meaning of a sentence is determined by its words and how they’re related to each other, i.e., the grammatical rules. Tokenization helps this process by allowing the machine to interpret individual texts, their definitions, and how they form the entire sentence’s meaning.
Part of that interpretation process is parts-of-speech tagging (POS tagging). Parts of speech are lexical categories assigned to every word in the dictionary. For example, nouns, adjectives, verbs, conjunctions, and so on.
Tagging parts of speech to each token is useful for understanding the semantic relationship between each word. POS tagging also helps with other text analytics tasks such as named entity recognition (e.g., California = Location).
After separating sentences into tokens and tagging their respective parts of speech, the text analysis machine will determine the syntactic structure. Simply put, syntactic structure is how strings of words in a sentence relate to each other.
Text analytics (and NLP) models often create a parse tree to represent these relationships between each token. This parse tree is useful for determining the semantics (meaning) of a sentence.
In other words, it helps the computer to understand inferred meanings of a message just like a human would. This step is important because words have different definitions, and they change according to context and regional dialects.
As an illustration, we immediately understand the meaning of “the apple dropped on the Apple” by interpreting what “apple” and “Apple” mean. Parsing is basically a machine’s way of doing the same thing.
Another important aspect of making a text analytics model understand text data is lemmatization and stemming. Lemmatization and stemming both involve tracing a word into its base form. That said, there is a slight difference in both methods’ approaches in doing that.
Stemming only removes the prefixes, suffixes, and infixes of a word. These are the “pre-”, “-ing”, and “-ed” of a word. However, stemming blindly trims these affixes without considering a word’s morphology, which sometimes leads to horrendous results.
On the other hand, lemmatization takes into account the morphology of a word (how a word is formed based on its etymology) when tracing its root form (also called lemma).
Here is an example to illustrate the difference between lemmatization and stemming:
Stopwords refer to common words that contribute little semantic information to the overall sentence. For example, a, the, at, is, etc. By eliminating stopwords, the machine can focus on more important words of a text and provide more accurate analyses.
While stopwords are helpful in cleaning out text datasets, the specific stopwords to remove are heavily dependent on the task at hand. Removing stopwords is also useful for spam filtering and sentiment analysis.
These tasks do not need these extra words and can benefit from a smaller dataset for quicker and more accurate analyses.
Text normalization refers to standardizing variations of a word into one form. There are many ways to express a term, especially online. One common way is to shorten words, such as writing “tomorrow” as “tmrw”.
While both terms share the same meaning, the different spellings may register as different things in the algorithm, resulting in varying analysis results.
Some terms that require standardization include numbers (one, 1), symbols (and, &), money ($, USD, dollars), and abbreviations (why, y). Text normalization is highly important in the clinical field as different medical practitioners take clinical texts differently.
Lowercasing is part of text normalization and involves converting all capital letters to lower case. Most of the lowercasing is done to named entities, such as converting “Canada” into “canada”. Lowercasing and text normalization simplify the text analytics process and thus improve the final results.
Text extraction and text classification are two big subtopics that have their own nuances and techniques involved. Generally, text extraction refers to machine learning techniques to draw out important terms or phrases.
One such task is identifying named entities such as brands and people. Named entity recognition is a common natural language processing task because it basically tells you what topic matters the most.
You don’t only have to identify named entities; the specific word you would like to extract depends on your organizations’ needs. Other words that you can highlight include product aspects (e.g., size, price, brand).
On the other hand, text classification refers to categorizing the extracted text into predefined tags. For example, “Elon Musk” can be classified as “People”. You can also customize these tags according to your needs, such as by sentiment (positive, neutral, negative) or by intent (interested, spam, query, etc.)
After the text analytics model has processed the data, it will visualize the key information in some manner. How the information is presented depends on your specific text analytics software.
Common ways text analytics software presents key insights include word clouds and sentiment graphs. In this case, Speak shows users the text data’s overall sentiment and prevalent topics at a glance.
Our interactive dashboard also allows you to customize the insights categorization according to your needs. Furthermore, our centralized database allows you to search for any keyword or topic across all media and media types, be it audio, video, or text.
Overall, our media library doesn’t just accurately extract key insights but is also optimized for searchability to increase operational efficiency, accessibility, and lower costs.
If you’d like to learn more about how you can take your organization to the next level with text analytics,, contact us at firstname.lastname@example.org or sign up for our 14-day trial with no credit card required.
Text mining is a machine that provides valuable data to your organization. However, information is only useful when they are accurately interpreted and put to use in the right manner. Data interpretation is in itself a broad topic with many techniques and case studies.
An inaccurate interpretation of market research data could result in costly mistakes. Coors, an established player in the beer industry, introduced Rocky Mountain Sparkling Water in 1990. At the time, bottled water was a trending product and so it made sense to capitalize on that.
Coors thought that by leaving their logo on the bottled water packaging, they could leverage their brand reputation to increase sales.
Naturally, people got confused and concerned about driving after consuming a product they associated with beer.
Perhaps if Coors had the opportunity to utilize text analytics tools at the time to better examine the text correlation between ‘Coors’, ‘beer’, and ‘water’, they might have introduced an incredible product rather than one they discontinued shortly after.
Text mining is using NLP machines to process and extract information from large amounts of unstructured text data. Despite being a fairly recent innovation, many organizations are increasingly adopting text mining in their operations.
No matter what industry the organizations are in, there are 5 recurring themes in regards to the benefits of text mining:
No matter how well you train your researchers, there are bound to be human errors. These errors are further amplified when accompanied by factors such as emotional stress, distractions, and fatigue.
Computers aren’t perfect either, but they’re far more reliable in analyzing a constant flow of data. One large reason is that machines aren’t limited by the aforementioned human restraints.
Thus, text analytics tools are effective in situations where mistakes could lead to costly consequences. An example would be analyzing text data in the healthcare industry, where one inaccurate diagnosis can result in loss of life.
Automated text analysis can process more data at greater speeds than human researchers. This allows you to achieve economies of scale, increase your bottom line, and improve ROI.
To that end, many researchers are using text analysis to process and identify patterns from hundreds of feedback forms.
In the same token, increased efficiency opens the opportunity to scale up your business. Given the sheer volume of unstructured text data available, it could take a team of human researchers several months, or even years to analyze all that data.
In contrast, text analysis tools can process hundreds of text documents within a day. Since organizations can now analyze the same amount of corpus in record speed, they can now scale up their research efforts and drastically improve productivity.
Thanks to advancements in NLP, AI, and text analytics, we can now gather and process vast amounts of data efficiently. Back then, the sheer volume of unstructured data meant that collecting them all was near-impossible, let alone analyzing them for insights.
Furthermore, the amount of unstructured data is ballooning thanks to the rising numbers of the Internet and social media users. Text analytics and machine learning is the key to accessing these ever-increasing data and transforming them into actionable insights.
Text analytics allows us to uncover patterns in text documents that may not be obvious at first glance. Moreover, the sheer amount of text documents to process adds to the noise and makes it harder to identify any underlying trends.
For instance, text analysis allows us to single out prevalent keywords in a text document. With that information in hand, you can then make more informed decisions and meet your customers’ needs more effectively.
Text analysis can be done through many methods and techniques. Different organizations utilize different techniques according to their needs. Every text analysis software also provides different features.
Naturally, more powerful tools are more expensive so make sure to assess your needs first before subscribing to any service. To give you a better idea of how to leverage text analysis in your organization, we’ll show you five common text analysis techniques which are:
Sentiment analysis is the process of analyzing a text document and determining its polarity (positive, neutral, negative). You can also use sentiment analysis to recognize emotions from text data. These emotions can be happy, sad, angry, or unsure.
Sentiment analysis is also the most common technique used in text analytics, and oftentimes accompany each other due to their similar natures. By analyzing the sentiment of a text corpus, you can dig deeper into the underlying meanings of a message and find out why they said it.
Named entity recognition refers to detecting named entities and tagging them according to their respective categories. For instance, categorizing “Tom Cruise” as “People” and “Washington” as “Place”.
One advantage of named entity recognition is that it allows you to quickly assign a topic to a text document, such as blog articles. To illustrate, recurring entities (e.g., Michael Jordan) indicate an interest in a certain topic (e.g., basketball, NBA).
News publications and e-commerce sites are already using this technology to provide relevant product recommendations. In fact, McKinsey reported that Amazon’s recommendations drive up to 35% of its sales.
To get a better understanding of how sentiment analysis and NER works, why don’t you try our text analysis tools below!
Similar to NER, topic analysis involves identifying recurring words and their associated categories. Then, the algorithm will assign a topic to that text data.
Take basketball, for example, repeated mentions of basketball players and related terms indicate that the text is talking about basketball.
Topic analysis shines on important areas that you should focus on. Say, if customers frequently bring up customer service, it’s a sign that you should perhaps improve your CRM!
Topic analysis also provides insights into your customers’ activities, interests, and opinions (AIOs). Equipped with that data, you can then craft more effective marketing strategies that target their topics of interest.
Other applications of topic analysis include tagging a category to incoming messages (e.g., spam), which is helpful in email marketing and customer service.
Word frequency is a simple text analytics technique, and it basically identifies the word count of a word or named entity. Naturally, a word that is frequently repeated denotes higher importance.
Also known as text clustering, word grouping involves organizing words that frequently appear next to each other. Common examples include grouping “good”, “bad”, and “customer service”.
Word grouping allows you to quickly filter out important issues from large volumes of text data, resulting in saved time and effort.
To quickly recap: text analytics refers to automatically processing large amounts of unstructured text data quickly and efficiently. Text analytics has various techniques, including sentiment analysis, named entity recognition, topic analysis, and word frequency.
But how exactly can you apply text analytics based on your specific needs? To give you a better idea, we’ll provide six applications of text analysis which are:
Running a social media account is tiring and it involves data analytics, replying to messages, keeping up with trends, content creation, and so on. These tasks are important but they make it difficult to scale your SMM efforts, especially when expanding to different social networks.
With text analytics, you can automate some of those tasks such as data collection and brand monitoring. Since social media is filled with unstructured text data, you can easily mine them for all kinds of insights.
For example, you can extract and analyze Tweets to determine trending topics or keywords. Once you’ve found a topic cluster, you can craft content strategies around them and increase engagement.
You can also use text analytics for reputation management and brand monitoring. Customer gripes are easily solvable but when left unchecked, could transform into a PR crisis and cost you millions of dollars and customer lifetime value.
With text analysis tools, you can quickly identify negative social media comments and address them immediately. At the same time, you can also capitalize on positive comments to improve your customers’ experience with your brand.
The success of your organization is directly correlated with how well you understand your customers.
It’s not just their demographics and psychographics either, you must thoroughly understand what consumers think of your brand and market offering. That’s where Voice of Customer comes in.
Voice of Customer refers to what customers are saying about your products and service. More specifically, understanding their experiences, expectations, and preferences.
There are many ways to gather VOC, the most common being social media, surveys, emails, and purchasing behavior. These sources provide a wealth of data and are easily accessible.
However, only collecting information isn’t sufficient – data needs to be transformed into insights to be useful. Text analytics and sentiment analysis dive deeper into finding out why consumers are talking about a certain subject.
Text analysis allows you to identify prevalent keywords and topics from a dataset. Then, using sentiment analysis tools, you can determine what customers think about that topic. For instance, identifying that customers have a negative sentiment towards your product’s price.
After text analysis has highlighted what areas to improve on, you can then focus your resources on said areas.
Market research goes hand-in-hand with discovering VOC. Data collection is a huge part of the market research process and requires a substantial sample size. If not, there simply won’t be enough data to inform decision-making.
At the same time, the amount of data to be analyzed can be overwhelming for humans. Text analytics models can process hundreds of text data sets and identify trends and patterns.
As a result, researchers can obtain a holistic overview of what customers are saying and improve decision-making.
You can also leverage text analysis in competitor research by analyzing what their customers are saying about them. Do they have gaps in their customer service? Or perhaps they aren’t meeting certain customer needs?
All this information is crucial for enhancing your business strategy, and may very well be the deciding factor between your and your competitors.
Obtaining high-quality leads can be time-consuming, and is often the most difficult part of lead generation. You have to create cold pitches, meet with potential prospects, and identify prospect sources, among other things.
As a result, precious time is wasted on administrative tasks which in turn, affects the bottom line. Text analysis models will automate all the menial tasks and improve the sales funnel processes.
For instance, tagging sentences in call transcripts and analyzing the prominence of those tagged terms. If unsuccessful prospects have a correlation with, say, assurance, then it’s time to look into that.
Other ways you can source leads include social media – the most common application for text analytics. Simply run your text analysis model through social media messages and pick out those that express buying intent. Then, you can focus your efforts on these high-quality leads instead of simply cold calling a prospect.
You can even run your text analytics model through your CRM to better serve your existing customers. For example, by identifying patterns among disgruntled and happy customers.
Working in healthcare is one of the most difficult jobs not only because of the expertise required, but the effort of documenting, organizing, and sorting through text data.
From patient health records, diagnosis records, transcript records – the number of text documents being created each day is borderline unmanageable.
Fortunately, as with all text data, you can run a text analytics model through them. This opens up a world of benefits as healthcare providers can automate tasks, allowing them to spend more time with their patients.
One application of text analytics in healthcare is utilizing NER to classify specific terms according to their categories, such as “insulin” and “treatment”. You can customize these terms and their categories according to your specific needs.
Aside from administrative purposes, text analytics also provides you with a holistic view of a patient’s health journey. By highlighting patterns in medical records, you can then provide a more accurate diagnosis for future patients.
Educators can benefit from text analytics by increasing operational efficiency. Educational institutions involve massive amounts of text data such as exam sheets, student feedback, emails, schedules, student records, and so on.
One application is to run a text analysis model through student feedback forms and identify trends and patterns. By finding out key concerns and addressing them, you’ll be able to increase survey response rates and ultimately, student retention.
Students too can benefit from text analytics, especially those in their higher education. Masters and Ph.D. students working on their thesis may be overwhelmed by dozens or even hundreds of interview transcripts.
Going through these transcripts can take hours and leave you fatigued. With text analytics tools, you can quickly extract key points from the transcripts and use them in your thesis.
If you’re interested to know more about text analytics, we’ve compiled a list of helpful resources for you to explore.
These resources are great if you want to experiment with creating your own text analysis model, or if you simply want to learn more about the topic.
If you’d like to build a text analytics model, you should familiarize yourself with Python NLTK and R. Those are some of the most common programming languages in text analytics and in NLP.
Since Python and R are some of the most common programming languages, their thriving community has built a comprehensive set of resources. These resources include video tutorials, datasets, online courses, forums, and more.
Most of these resources are even available online for free! In other words, anyone can now learn natural language processing and text analytics in the comfort of their homes.
All you need is a working laptop, determination, and to continue reading on our recommended text analytics resources.
We recommend you follow this text analytics tutorial by Datacamp. Datacamp is an online platform to learn almost everything about data science, and many of its courses are created with beginners in mind.
One such tutorial is Text Analytics for Beginners using NLTK. Even though text analytics (and data science in general) is a complicated topic, this tutorial breaks the topic down into simple sections that even programming greenhorns can understand.
Moreover, the tutorial features copy pastable codes to make your learning progress easier. Then, once you’ve gotten better at text analysis, you can apply your newfound knowledge to real-world projects by Datacamp. For example, text mining data from Jeopardy, the game show.
Text analytics models must be fed with a large number of precise training datasets. Machine learning algorithms learn the same way as humans do: the more information they consume, the faster they improve.
We recommend this curated list of dataset collections by UCI ICS, the 25th ranked undergraduate school for computer science in the US.
In this list, you can find tons of interesting datasets, including IMDb movie reviews, product reviews, and Yelp reviews. Do note that the collection is just a small example of the many datasets available online.
Feel free to explore more datasets from reliable sources (e.g., Kaggle, Github) or even create your own!
Aside from the tutorials mentioned above, there are also online courses and video series available to advance your learning. These courses vary in costs and prerequisites.
If you’re completely new to text analytics, we recommend this YouTube video series by Dave Langer of Data Science Dojo. It is a comprehensive 12-video playlist that covers everything from introductory concepts to advanced mathematical calculations.
You can also try out this Udemy course on Machine Learning using Python and R. The course requires about 44 hours of time commitment and awards a certificate upon completion. Moreover, it is highly affordable and you can progress at your own pace.
Once you’ve established your fundamentals in machine learning and NLP, you can advance to this NLP course by Stanford Online. Since text classification goes hand-in-hand with natural language processing, learning NLP will be beneficial, especially if you’re pursuing a career in data science.
That said, Stanford Online’s course has certain prerequisites that you must attain before enrolling. Upon completion of the course, you’ll be awarded a certificate that you can use to boost your CV.
Text analytics is the process of transforming large amounts of unstructured text into quantitative data before extracting key information from it. It utilizes common NLP techniques such as named entity recognition and sentiment to provide actionable insights to benefit your organization.
In light of recent technological advancements and the ongoing Fourth Industrial Revolution, text analytics and NLP machine learning models are now everyday solutions used by organizations. The cut-throat world of marketing has become even more intense as companies scramble to find ways to outcompete one another.
Moreover, the amount of data is only increasing as new social media platforms such as TikTok spread and expand their user base.
With all that unutilized unstructured data online and the text analytics tools available, one thing seems certain: effective data analysis is now a viable core advantage for businesses to stand out from the competition.
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