Text Analysis is a type of data analysis in which the content and meaning of texts, as well as their structure and discourse, are thoroughly examined and classified into meaningful components. This type of analysis has become increasingly critical for businesses in the past decade due to its efficiency in extracting valuable customer information.
Given the size of the audiences, people who utilize online platforms no longer produce text only for personal communication. These days, people freely share their opinions on any matter to get their thoughts heard by anyone on the internet. Thus, technology, the internet, and especially social media have given rise to an unprecedented amount of text production by a bevy of consumers.
If you know how to analyze text, the information you receive from it may positively influence the course of your business in many ways—from social media listening and text Sentiment Analysis to crisis management and customer journey mapping. But, not everyone can be an expert on text analyzer.
Yet, do not fret! We have good news: technology has risen to the occasion once again.
Along with the rise of the written words online, research technologies have developed ways in which you can extract valuable insights from these exponentially growing amounts of unstructured texts. With a Text Analysis tool, you no longer have to process and organize excerpts manually to understand the content—this would be inefficient and often inaccurate. Instead, analyzing a text can be as easy as dragging and dropping a data set into a text analyzing software, such as Kimola Cognitive, to see immediate and automatic results!
Now that we got the main idea, what does it mean to analyze a text? Let us explain in detail…
Text is data, and when it comes to data analysis, there are two types: qualitative and quantitative data.
As the name implies, quantitative data deals with “quantity.” The more straightforward one of the two types, quantitative data presents numerical variables that can be counted and measured using numbers and statistics. Thanks to its structured nature, it is typically close-ended and precise. When conducted correctly, quantitative data analysis is irrefutable.
Amounts, volumes, sizes, and costs of objects and actions can all be considered quantitative data. Businesses can acquire quantitative data via online platforms, statistical market research, and Likert scale customer surveys.
While quantitative data is essential, with the aforementioned rise in textual information, qualitative data has also taken center stage in recent years. And again, as the name suggests, qualitative data concerns “quality.” It focuses on the more explorative question of “why?” Therefore, it is unstructured and subjective. When valid, the results of qualitative data analysis are open-ended and malleable in that they require the human factor to procure meaning.
As the more chaotic of the two types of data, qualitative data is anything that quantitative is not: words, audio, and visual documents are all considered qualitative data. Traditionally, businesses conduct focus groups, case studies, and surveys with open-ended questions to obtain qualitative data.
Textual data can be analyzed in two manners: Text Analysis and Text Analytics. Text Analytics works exclusively with the quantity and does not glean any qualitative outcome, and it is the derivation of numerical values to present patterns found in the texts.
For instance, Text Analytics can find patterns amongst hospital and doctor’s reports in the healthcare industry, which would provide quantitative data regarding necessary supplies at a hospital, changes in the usage of insurance policies, and even recurring or common symptoms.
Text Analysis, on the other hand, is the extraction of qualitative data from texts by decoding and categorizing the complex nature of natural languages. In this sense, the deciphered language will reveal the quality of whatever is being analyzed and will require the input of the human mind to gain meaning.
With Text Analysis, the example mentioned above from the healthcare industry would gain even more in-depth and insightful information. For instance, by analyzing hospital and doctor’s reports, Text Analysis techniques could reveal how successful certain services have been, how the changes in insurance policies have impacted patient experiences, and what underlying reason may have caused common symptoms.
Artificial Intelligence and Machine Learning are at the core of Text Analysis, which essentially converts unstructured qualitative data of our everyday language into an unambiguous and manageable format through Natural Language Processing (NLP) features like the Named Entity Recognition (NER) and presents it in various forms of data visualization and report to automate the processes that go into textual data management.
You can also learn more about how to generate an NLP dataset for Text Analysis or how Text Analysis will help your business analyze social media conversations with AI.
As a very versatile form of investigation, Text Analysis has a wide array of applications that take advantage of the developing technologies used behind the scenes.
Let’s first look at Text Classification as it is perhaps the most utilized application of Text Analysis.
As we mentioned, qualitative data such as texts are unstructured on the internet. Text Classification is an NLP technique in which gathered textual data are assigned tags and categories according to their content and cataloged into meaningful classification models. Thus, Text Classification, which brings the necessary structure to the otherwise unmanageable text data, is the backbone of any Text Analysis tool.
How the news articles are presented in your favorite news app in specific sections and how some of your emails are sent off to the desolate spam folder, or how some of the comments in the comments section of a social media post are flagged as inappropriate are three of the most common ways in which our life is affected by Text Classification.
How does Text Classification work?
At its most basic, Text Classification can classify textual documents into predetermined sets of topics by detecting keywords in a process called topic labeling, also known as topic classification. Text Classification can work in three ways: Rule-based, Machine Learning, and Hybrid.
This manner of Text Classification requires manually determined sets of linguistic rules and classes that will instruct the system to identify the appropriate category according to the textual data within the content.
Suppose a news app wants to divide its articles into sections like U.S. Politics, World News, Culture, and Sports. In that case, it must designate keywords for each group so that the computer system may classify the article to the related section based on the number of relevant words.
While this is the most regulated form of Text Classification, it is also extremely time-consuming, difficult to maintain, and prone to human error.
Machine learning not only learns but also continuously develops the rules of Text Classification according to past experiences, often depending on an example set of pre-labeled training data.
An ML algorithm needs to be fed a large amount of vectorized training data called “bag of words” to translate natural languages into computational ones. From this batch of exemplary data, the algorithm begins to produce classification models that evolve according to observations.
Compared to its rule-based counterpart, ML Text Classification is not only much easier to maintain but also more reliable and accurate, especially when it comes to large amounts of complex data input.
Sign up to Kimola Cognitive, the no-code machine learning platform to start classifying texts with a few clicks.
A hybrid text classifier combines the best of both worlds by merging a rule-based system with a machine learning-trained base. This system allows humans to make the necessary adjustments while harnessing the power of machine learning.
Now that most people are sharing their opinions on a particular product or service online, there are innumerous opportunities to learn more about the needs of your consumers and the developments in your industry.
Sentiment Analysis employs advanced machine learning algorithms to automatically scan and categorize texts according to positive, negative, and neutral sentiments as well as the writer's mood, emotions, and context. In very advanced cases, it can even detect sarcasm!
Topic Analysis (aka. topic detection, topic modeling, topic labeling, or topic extraction) is a Natural Language Processing (NLP) technique that extracts meaning from a large set of textual data by assigning “tags” or categories based on the recurring themes or subjects in each text.
Topic Analysis helps find information surrounding a specific theme, business, or product since it derives meaning from around certain topics. Combined with Sentiment Analysis, it can bring about more focused consumer insights.
Intent Detection or intention classification is the automatic discernment regarding the reasons behind a customer review. For this, you must tag strong vocabulary words that display a clear intent to act, such as “purchase,” “buy,” “unsubscribe,” and so on.
With machine learning, such features begin to not only automate the detection and classification of intent but also predict it. This comes in handy during customer journey planning.
Text Extraction is another popular form of Text Analysis as it efficiently collects already existing data from a given text.
Keywords are the words that give a text its meaning through volume and definition. Extracting keywords can help outline a text's content, get texts indexed during SEO, and present textual data in compelling data visualization formats like word clouds.
Named Entity Recognition (NER) is perhaps one of the most used features of NLP. NER (aka entity identification or entity extraction) is an NLP technology that automatically detects and categorizes named entities in texts. Entities are words or sequences of words that consistently refer to the same objects, such as proper nouns, quantities, monetary values, and so on.
In our everyday lives, NER is often used for content recommendations on media platforms such as streaming channels or to filter resumes during job recruitments.
In Text Analysis, the Word Frequency technique measures the words and or word groups that appear most often in texts. Word Frequency helps businesses gauge the vocabulary customers use to define them, their products, and their services at different touchpoints such as customer service interactions, the comments sections of e-commerce websites, or review platforms.
Collocation identifies the words that frequently appear together and make up a meaning separate from its constituting parts. For instance, “fast food” is a two-word collocation. Three-word collocations are often even more idiomatic. Take “burst into tears,” “draw a conclusion,” or “close a deal,” for example. They have meanings that an algorithm must learn to deduce from the whole. Otherwise, a singular analysis of each word would lead to confusion.
Concordance aids in determining the context and setting of a word or a combination of words. While establishing the concordance of a word, Text Analysis must evaluate the preceding context and the following context to determine how the word is used. If we were targeting the word “useful,” the following sentences would reveal how diverse the results of a word’s concordance can be:
The Word Sense Disambiguation feature of Text Analysis can distinguish words with multiple meanings, but only after training with representative datasets and learning from real-world observations.
For instance, a Text Analysis application would need to know both meanings of the word “bat” to understand these sentences clearly:
Text Clustering enables Text Analysis to comprehend and categorize large amounts of unstructured data. Because they don’t require tagged examples to train models, Text Clustering algorithms often work faster than classification algorithms, making them clumsier and more error-prone. This lack of training data is also known as unsupervised machine learning.
Search engines such as Google typically work with Text Clustering algorithms. With such a vast amount of textual data, search engines have no choice but to itemize unstructured online data into haphazardly grouped clusters of web pages according to a set of interchangeable words or n-grams (every possible permutation of contiguous words or letters in a text). Thus, the pages from the cluster with the highest number of words or n-grams relevant to the search query will come first in the results.
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