Unlocking the Power of AI: Demystifying the Importance of Training Datasets

 

What is Text Mining?

Text mining, also known as text analysis, is the transformation of unstructured text into structured data that can be analyzed easily. Natural language processing (NLP) is used in text mining. This allows machines to automatically understand human language and process it.

Businesses see the huge amount of data generated each day as both a challenge and an opportunity. Data can help companies gain smart insights into people's opinions on a product or service. Imagine all the ideas you could get by analyzing customer feedback, product reviews, social media posts and support tickets. The other side of the coin is the problem of how to process all that data. Text mining is a key component of this process.

Text mining, like many things in Natural Language Processing (NLP), may seem complicated. It doesn't have to be. This guide will explain the basics of text mining and its various techniques. It will also make it easy to understand how it works. Learn about the main uses of text mining, and how it can be used by companies to automate many processes.

Get Started with Text Mining

Text mining uses natural language processing to extract valuable insight from unstructured text. Text mining transforms data into information that machines understand. It automates the process for classifying texts by topic, sentiment, and intent.

Text mining allows businesses to quickly and efficiently analyze large and complex data sets. Companies are also using this powerful tool to automate repetitive tasks and save time. This allows customer service agents to concentrate on their core competencies.

Let's suppose you have tons of customer reviews on G2 Crowd and want to know what they think about your SaaS. You could use a text mining algorithm to identify the most frequently mentioned topics in customer comments and how people feel about them. It is possible to determine the most popular keywords that customers use in relation to a topic.

Text mining is a way for companies to make the most of their data. This results in better business decisions.

You may be asking yourself, "How does text mining do all this?" This is the answer to your question: machine learning.

Machine learning is an AI-derived discipline that focuses on the creation of algorithms that allow computers to learn tasks from examples. After being trained with data, machine learning models can predict with a certain degree of accuracy.

Automated text analysis is possible when text mining and machine-learning are combined.

Let's return to the previous example of SaaS review. We will now classify these reviews into topics such as UI/UX/Bugs, Pricing, Customer Support, or Pricing. You would first upload a few examples and tag them manually to train a topic classification model. After receiving several examples, the model can learn to distinguish topics and make associations. It can also start making its own predictions. You should give your models many examples that are representative for the problem you are trying to solve in order to get high accuracy.

We'll now see how text mining differs from other terms like text analysis or text analytics.

What is the difference between Text Mining, Text Analysis and Text Analytics?

Text analysis and text mining are often synonyms. However, text analytics is a different concept.

They both aim to solve the same problem (automatically analyzing raw text data collection) using different techniques. Text mining is a method that identifies the relevant information in a text, and provides qualitative results. Text analytics however, is more focused on finding patterns and trends in large data sets, which results in more quantitative results. Text analytics is used to create graphs, tables, and other types of visual reports.

Text mining is a combination of concepts from statistics, linguistics and machine learning. It creates models that learn from data and can predict new information based upon their past experience.

Text analytics uses the results of text mining models to create graphs and other data visualizations.

The type of information available will determine the best approach. Both approaches can be combined to produce more convincing results in most cases.

Methods and Techniques

Text mining can be done in many ways. We'll be covering some of the most common.

Basic Methods

Word frequency

To identify the most frequently used terms or concepts within a data set, word frequency can be used. It is particularly helpful when analyzing customer reviews, customer feedback and social media conversations to find the most frequently used words in unstructured text.

If you see the words "expensive, overpriced, and overrated" frequently in customer reviews, this could indicate that your prices or target market need to be adjusted. ).

Collocation

A collocation is a series of words that are often found near one another. Bigrams (a pair or words that are most likely to be used together, such as get started, save your time, or decision making) are the most popular types of collocations. Trigrams (a combination or three words like within walking distance, keep in touch) are also common.

Collocations can be identified and counted as one word. This improves the granularity and image data collection allows for a better understanding and interpretation of the text's semantic structure. In the end, it leads to more precise text mining results.

Concordance

Concordance can be used to identify the context in which a word, or group of words appear. The human language is ambiguous. A single word can be used in multiple contexts. The context can be used to help you understand the exact meaning of a word by analyzing its concordance.

Advanced Methods

Text classification

Text classification refers to the process of assigning tags (or categories) to unstructured text data. Natural Language Processing (NLP), which is essential for organizing and structuring complex text into meaningful data, makes this task easy.

Text classification allows businesses to quickly and economically analyze any type of information, including emails and support tickets.

Below are some of the most common tasks in text classification: topic analysis, sentiment analysis and language detection.

Topic Analysis: This helps you to understand the major themes and subjects in a text. It is also one of the most important ways to organize text data. A support ticket stating that my online order is not yet received can be classified under Shipping Issues.

Sentiment Analysis is the analysis of emotions underlining any text. Let's say you analyze a collection of reviews about your mobile application. It may be that UI-UX and ease of use are the most popular topics mentioned in these reviews. However, this is not enough information to draw any conclusions. Sentiment analysis allows you to understand what people are thinking and feeling, and then classify them as neutral, positive, or negative. The many uses of sentiment analysis in business are numerous, including the ability to analyze social media posts and review support tickets. For customer service, it might be possible to identify angry customers quickly and priorities their problems.

Language Detection allows you to classify text based upon its language. It is able to automatically route support tickets to the correct team based on their location. This is one of its most valuable applications. This task can be automated easily and teams can save valuable time.

You could use a text classification system to automatically recognize the intent or purpose of a message. This is especially useful for analyzing customer conversations. You could, for example, sort through outbound sales emails to identify prospects who are most interested in your product.

Text Extracting

Text extraction is a text-analysis technique that extracts key pieces from text. This includes keywords, entity names and addresses. Text extraction allows companies to avoid the tedious task of manually sorting through data in order to extract key information.

It is often possible to combine text classification and text extraction in one analysis.

We'll be referring to the main tasks of text extract - keyword extraction and named entity recognition.

Keyword extraction: Keywords are the most relevant terms in a text. They can be used to summarize the content. A keyword extractor can be used to index data, summarize text, or create tag clouds.

Named entity recognition: This allows you to identify and extract names from text that refers to companies, organizations, or individuals.

Feature extraction: This allows you to identify the specific characteristics of a product/service in a collection of data. If you analyze product descriptions, you can easily extract features such as color, brand, and model.

Why is text mining important?

Every day, individuals and companies generate a lot of data. Stats show that nearly 80% of all existing text data is not structured. This means it's difficult to organize, searchable and manage. It's useless.

Companies face a challenge and major concern in being able to extract relevant information from raw data. This mission is possible only by text mining.

Unstructured text data is important in a business context. It can include chats, emails, social media posts and support tickets. It is often difficult to sort through all this information manually. It's not only time-consuming, expensive, and difficult to scale up but it is also inaccurate and impractical.

However, text mining has proven to be reliable and cost-effective in achieving accuracy, scalability, and fast response times. These are just a few of the main benefits:

Scalability: Text mining allows you to quickly analyze large amounts of data. Companies can save time and allow them to concentrate on other tasks by automating certain tasks. This leads to more productive businesses.

Text mining allows companies to prioritize urgent matters in real-time. This includes detecting potential crises and identifying product flaws and negative reviews. This is why it's so important. Because it allows companies take immediate action.

Consistent Criteria: People are more likely make mistakes when they work on repetitive tasks. It is also difficult for them to keep their data consistent and interpret it subjectively. Let's look at tagging. Most teams find adding categories to support tickets or emails is a tedious task that can lead to inconsistencies and errors. This task can be automated to save time, get more precise results, and ensure that every ticket is treated the same way

What is text mining?

Text mining allows you to analyze large quantities of data and uncover relevant insights. It can be combined with machine learning to create text analysis models which learn to classify and extract specific information based upon previous training.

Text mining can be very simple, even though it may seem complicated.

Gathering data is the first step in text mining. Let's suppose you want to analyze the conversations between users using Intercom's live chat. First, you will need to create a document that contains this data.

Data can be either internal (interactions via chats, emails or surveys), or external (information gleaned from social media sites, review sites, news outlets and other websites).

Preparing your data is the second step. To build your inputs for your machine learning model, text mining systems employ several NLP techniques, including tokenization, stop removal, stemming, lemmatization and stemming.

Next, we'll get to the text analysis. This section will explain the differences between text classification and extraction.

How GTS can help you?

Global Technology Solutions understands the need of having high-quality, precise datasets to train, test, and validate your models. As a result, we deliver 100% accurate and quality tested datasets. Image datasets, Speech datasets, Text datasets, ADAS annotation and Video datasets are among the datasets we offer. We offer services in over 200 languages.