Soon after you wake up, you usually navigate through large amounts of textual data in the form of text messages, emails, social media updates, and blog posts before you make it to your first cup of coffee.
Deriving information from such large volumes of text data is challenging. Businesses deal with massive quantities of text data generated from several data sources, including apps, web pages, social media, customer reviews, support tickets, and call transcripts.
To extract high-quality, relevant information from such huge amounts of text data, businesses employ a process called text mining. This process of information extraction from text data is performed with the help of text analysis software.
Text mining, also called text data mining, is the process of analyzing large volumes of unstructured text data to derive new information. It helps identify facts, trends, patterns, concepts, keywords, and other valuable elements in text data.
It's also known as text analysis and transforms unstructured data into structured data, making it easier for organizations to analyze vast collections of text documents. Some of the common text mining tasks are text classification, text clustering, creation of granular taxonomies, document summarization, entity extraction, and sentiment analysis.
Text mining uses several methodologies to process text, including natural language processing (NLP) .
Natural language processing (NLP) is a subfield of computer science, linguistics, data science, and artificial intelligence concerned with the interactions between humans and computers using natural language.
In other words, natural language processing aims to make sense of human languages to enhance the quality of human-machine interaction. NLP evolved from computational linguistics, enabling computers to understand both written and spoken forms of human language.
is the NLP market's estimated worth by 2025.
Many of the applications you use have NLP at their core. Voice assistants like Siri, Alexa, and Google Assistant use NLP to understand your queries and craft responses. Grammarly uses NLP to check the grammatical accuracy of sentences. Even Google Translate is made possible by NLP.
Natural language processing employs several machine learning algorithms to extract the meaning associated with each sentence and convert it into a form that computers can understand. Semantic analysis and syntactic analysis are the two main methods used to perform natural language processing tasks.
Semantic analysis is the process of understanding human language. It's a critical aspect of NLP, as understanding the meaning of words alone won't do the trick. It enables computers to understand the context of sentences as we comprehend them.
Semantic analysis is based on semantics – the meaning conveyed by a text. The semantic analysis process starts with identifying the text elements of a sentence and assigning them to their grammatical and semantical role. It then analyzes the context in the surrounding text to determine the meaning of words with more than one interpretation.
Syntactic analysis is used to determine how a natural language aligns with grammatical rules. It's based on syntax , a field of linguistics that refers to the rules for arranging words in a sentence to make grammatical sense.
Some of the syntax techniques used in NLP are:
Most businesses have the opportunity to collect large volumes of text data. Customer feedback, product reviews, and social media posts are just the tip of the big data iceberg. The kind of ideas that can be derived from such sources of textual (big) data are profoundly lucrative and can help companies create products that users will value the most.
Without text mining, the opportunity mentioned above is still a challenge. This is because analyzing vast amounts of data isn't something the human brain is capable of. Even if a group of people tries to pull off this Herculean task, the insights extracted might become obsolete by the time they succeed.
of enterprise data is unstructured.
Text mining helps companies automate the process of classifying text. The classification could be based on several attributes, including topic, intent, sentiment, and language.
Many manual and tedious tasks can be eliminated with the help of text mining. Suppose you need to understand how the customers feel about a software application you offer. Of course, you can manually go through user reviews, but if there are thousands of reviews, the process becomes tedious and time-consuming.
Text mining makes it quick and easy to analyze large and complex data sets and derive relevant information from them. In this case, text mining enables you to identify the general sentiment of a product. This process of determining whether the reviews are positive, negative, or neutral is called sentiment analysis or opinion mining .
Further, text mining can be used to determine what users like or dislike or what they want to be included in the next update. You can also use it to identify the keywords customers use in association with certain products or topics.
Organizations can use text mining tools to dig deeper into text data to identify relevant business insights or discover interrelationships within texts that would otherwise go undetected with search engines or traditional applications.
Here are some specific ways organizations can benefit from text mining:
In short, text mining helps businesses put data to work and make data-driven decisions that can make customers happy and ultimately increase profitability.
Text mining and text analysis are often used synonymously. However, text analytics is different from both.
Simply put, text analytics can be described as a text analysis or text mining software application that allows users to extract information from structured and unstructured text data.
Both text mining and text analytics aim to solve the same problem – analyzing raw text data. But their results vary significantly. Text mining extracts relevant information from text data that can be considered qualitative results . On the other hand, text analytics aims to discover trends and patterns in vast volumes of text data that can be viewed as quantitative results .
Put differently; text analytics is about creating visual reports such as graphs and tables by analyzing large amounts of textual data. Whereas text mining is about transforming unstructured data into structured data for easy analysis.
Text mining is a subfield of data mining and relies on statistics, linguistics, and machine learning to create models capable of learning from examples and predicting results on newer data. Text analytics uses the information extracted by text mining models for data visualization.
Numerous text mining techniques and methods are used to derive valuable insights from text data. Here are some of the most common ones.
Concordance is used to identify the context in which a word or series of words appear. Since the same word can mean different things in human language, analyzing the concordance of a word can help comprehend the exact meaning of a word based on the context. For example, the term "windows" describes openings in a wall and is also the name of the operating system from Microsoft.
As the name suggests, word frequency is used to determine the number of times a word has been mentioned in unstructured text data. For example, it can be used to check the occurrence of words like "bugs," "errors," and "failure" in the customer reviews. Frequent occurrences of such terms may indicate that your product requires an update.
Collocation is a sequence of words that co-occur frequently. "Decision making," "time-consuming," and "keep in touch" are some examples. Identifying collocation can improve the granularity of text and lead to better text mining results.
Then there are advanced text mining methods such as text classification and text extraction . We'll go over them in detail in the next section.
Text mining is primarily made possible through machine learning. Text mining algorithms are trained to extract information from vast volumes of text data by looking at many examples.
The first step in text mining is gathering data. Text data can be collected from multiple sources, including surveys, chats, emails, social media, review websites, databases, news outlets, and spreadsheets.
The next step is data preparation. It's a pre-processing step in which the raw data is cleaned, organized, and structured before textual data analysis. It involves standardizing data formats and removing outliers, making it easier to perform quantitative and qualitative analysis.
Natural language processing techniques such as parsing, tokenization, stop word removal, stemming, and lemmatization are applied in this phase.
After that, the text data is analyzed. Text analysis is performed using methods such as text classification and text extraction. Let's look at both methods in detail.
Text classification, also known as text categorization or text tagging, is the process of classifying text. In other words, it's the process of assigning categories to unstructured text data. Text classification enables businesses to quickly analyze different types of textual information and obtain valuable insights from them.
Some common text classification tasks are sentiment analysis, language detection, topic analysis, and intent detection.
Now, let's take a look at the different types of text classification systems.
Rule-based text classification systems are based on linguistic rules. Once the text mining algorithms are coded with these rules, they can detect various linguistic structures and assign the correct tags.
For example, a rule-based system can be programmed to assign the tag "food" whenever it encounters words like "bacon," "sandwich," "pasta," or "burger".
Since rule-based systems are developed and maintained by humans, they're easy to understand. However, unlike machine learning-based systems, rule-based systems demand humans to manually code prediction rules, making them hard to scale.
Machine learning-based text classification systems learn and improve from examples. Unlike rule-based systems, machine learning-based systems don't demand data scientists to code the linguistic rules manually. Instead, they learn from training data that contains examples of correctly tagged text data.
Machine learning algorithms such as Naive Bayes and Support Vector Machines (SVM) are used to predict the tag of a text. Many a time, deep learning algorithms are also used to create machine learning-based systems with greater accuracy.
As expected, hybrid text classification systems combine both rule-based and machine learning-based systems. In such systems, both machine learning-based and rule-based systems complement each other, and their combined results have higher accuracy.
A text classifier's performance is measured with the help of four parameters: accuracy , precision , recall , F1 score .
Another way to test the performance of a text classifier is with cross-validation .
Cross-validation is the process of randomly dividing the training data into several subsets. The text classifier trains on all subsets, except one. After the training, the text classifier is tested by making predictions on the remaining subset.
In most cases, multiple rounds of cross-validation are performed with different subsets, and their results are averaged to estimate the model's predictive performance.
Text extraction, also known as keyword extraction, is the process of extracting specific, relevant information from unstructured text data. This is mainly done with the help of machine learning and is used to automatically scan text and obtain relevant words and phrases from unstructured text data such as surveys, news articles, and support tickets.
Text extraction allows companies to extract relevant information from large blocks of text without even reading it. For example, you can use it to quickly identify the features of a product from its description.
Quite often, text extraction is performed along with text classification. Some of the common text extraction tasks are feature extraction, keyword extraction, and named entity recognition.
Regular expressions and conditional random field (CRF) are the two common methods of implementing text extraction.
Regular expressions are a series of characters that can be correlated with a tag. Whenever the text extractor matches a text with a sequence, it assigns the corresponding tag. Similar to the rule-based text classification systems, each pattern is a specific rule.
Unsurprisingly, this approach is hard to scale as you have to establish the correct sequence for any kind of information you wish to obtain. It also becomes difficult to handle when patterns become complex.
Conditional random fields (CRFs) are a class of statistical approaches often applied in machine learning and used for text extraction. It builds systems capable of learning the patterns in text data that they need to extract. It does this by weighing various features from a sequence of words in text data.
CRFs are more proficient at encoding information when compared to regular expressions. This makes them more capable of creating richer patterns. However, this method will require more computational resources to train the text extractor.
You can use the same metrics used in text classification to evaluate the performance of the text extractor. However, they’re blind to partial matches and consider only exact matches. Due to that reason, another set of metrics called ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is used.
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The amount of data managed by most organizations is growing and diversifying at a rapid pace. It's nearly impossible to take advantage of it without an automated process like text mining in place.
An excellent example of text mining is how information retrieval happens when you perform a Google search. For example, if you search for a keyword, say "cute puppies," most search results won't include your exact query.
Instead, they'll be synonyms or phrases that closely match your query. In the example of "cute puppies,” you'll come across search engine page results that include phrases such as "cutest puppy,” "adorable puppies,” "adorable pups,” and "cute puppy".
This happens because text mining applications actually read and comprehend the body of texts, closely similar to how we do it. Instead of just relying on keyword matching, they understand search terms at conceptual levels. They do an excellent job of understanding complex queries and can discover patterns in text data, which is otherwise hidden to the human eye.
Text mining can also help companies solve several problems in areas such as patent analysis, operational risk analysis, business intelligence, and competitive intelligence.
Text mining has a broad scope of applications spanning multiple industries. Marketing, sales, product development, customer service, and healthcare are a few of them. It eliminates several monotonous and time-consuming tasks with the help of machine learning models.
Here are some of the applications of text mining.
Text mining or text analysis software solutions enable users to derive valuable insights from structured and unstructured text data. Insights can include patterns and themes, language, sentiment analysis, and key phrases. These tools use machine learning and natural language processing to automatically extract relevant information and facilitate data visualization for better interpretation.
To qualify for inclusion in the text analysis category, a product must:
*Below are the five leading text analysis software from G2's Summer 2021 Grid® Report. Some reviews may be edited for clarity.
RapidMiner is a software platform that offers an integrated environment for data preparation and text mining. It empowers users of different skill levels to rapidly build and operate AI solutions and create immediate business impacts.
"RapidMiner is very intuitive, especially to non-coders like myself. They also provided educational licenses for academic institutions, which is a big help to further educational use of predictive data analytics and to help foster advances in the academic fields. The RapidMiner community is also very active and helpful. The marketplace also provides valuable and timely updates and add-ons which benefit a wide range of needs."
"There isn't much I dislike about RapidMiner. The only thing that comes to my mind is the Python integration, which can be a bit hard to debug at times."
IBM Watson Studio is a leading machine learning and data science solution that empowers analysts, developers, and data scientists to create, run, and manage AI models. This tool speeds up data exploration and preparation and allows users to monitor models to reduce drift and bias.
"Every data scientist has many tools in his/her notebook, which is excellent for research and exploration. But when it comes to real-world projects, you need to simplify and integrate them. I found this the best thing in IBM Watson Studio - a simplified and integrated workbench for doing productive data science projects."
"The user interface of the Watson Studio is not very intuitive. Improvements can be made here. Additional tutorials can also be helpful."
Confirmit is a multi-channel software platform that helps companies conduct market research and understand customer and employee experience. It's a feature-rich solution that enables users to derive maximum value and insights from research and feedback projects. With Confirmit, businesses can collect data from an array of devices and use smart analytics tools to enhance the extracted insights.
"Confirmit's versatility enables you to create pretty much anything you can dream of. Script nodes make pretty much anything possible. I have tried other survey platforms, and though they may appear more user-friendly, the capabilities just aren't there at the end of the day. From even the simplest omnibus surveys to extremely complicated multi-country/multi-language, it is all possible in Confirmit. If you have a basic knowledge of any programming language, you can take Confirmit pretty far. If you are an experienced programmer, you'll have no problem using Confirmit to its limit."
"Although Confirmit has an excellent option to generate reports, learning how to use this functionality properly turns out to be quite cumbersome. All the processes to create and customize a report are very complex, so they should make this section a bit more intuitive."
Amazon Comprehend is an NLP service that enables users to discover valuable insights in unstructured data. This service can identify crucial elements in text data such as people, language, and places. It's useful to detect customer sentiment in real time, which can help businesses make better decisions to improve customer experience.
"What I like the most about Amazon Comprehend is that it can be integrated with other great AWS software like Amazon S3 and Glue. This makes it easier to facilitate the storage of our texts and documents for their previous analysis. Besides this, the pricing is reasonable because it only charges for the amount of text analyzed, so small and large companies can use Comprehend."
"The management interface lacks some functionality. Since it's a relatively new product, I expect that to change over time. As of now, you cannot manually delete jobs you no longer need."
Thematic helps companies analyze and understand customer feedback in-depth. Its proprietary AI-powered Thematic Analysis enables businesses to capture the real meaning in individual phrases and is also capable of grouping similar phrases into themes, even if they are worded differently.
“Thematic is a very intuitive tool to use. It boasts a robust level of granularity, allowing the user to see the general breadth of verbatim themes, dig into the sub-themes, and further into the sentiment of the open text itself. This, paired with the ability to filter the responses by segments, trend the data and themes over time, and visualize the impact of open text on KPIs such as NPS, makes it a potent tool for anyone looking to get insights.
My team and I have found using Thematic saves us time, which is critical when working against product timelines. This speed is both parts due to the tool's usability and the world-class support that Thematic offers to its users.
The Thematic customer success team shows tremendous compassion and always seeks to understand our specific needs from project to project. As an example of their support, because of the volume of text we analyze with Thematic, I needed a better way to keep track of the internal usage, and they built me a dashboard to do that!”
“Given that most of our work is in the Healthcare space where technical jargon and weird comments prevail, it took us longer than we would have liked to "train" the software in the initial setup phase.
It also took a lot of "hands-on" time to appreciate the value the solution has fully. Sharing this knowledge with time-poor customers so they can use the platform independently has been challenging.
Thematic has significantly improved the interface and knowledge base since we started and is always on hand to help. Even though we had these challenges, they've been manageable and worth the "pain" to get where we are today.”
The term "mining" might give you mental images of people digging holes or breaking rocks to extract valuable minerals. Text mining is not even slightly similar to it but can extract valuable information that can help companies augment their decision-making processes.
Data listens to no one. But if you listen closely to it, you might discover nuggets of information that can help find new ways to improve your products, enhance customer experience, and ultimately skyrocket your business's profitability.
Like how computers comprehend the written and spoken forms of human language, have you wondered how they try to understand the visual world? If so, then feed your curiosity by reading about computer vision.
Amal is a Research Analyst at G2 researching the cybersecurity, blockchain, and machine learning space. He's fascinated by the human mind and hopes to decipher it in its entirety one day. In his free time, you can find him reading books, obsessing over sci-fi movies, or fighting the urge to have a slice of pizza.
