We live in a more connected world than our forefathers and are currently witnessing an information boom. There is a ton of user-generated content on the internet that contains valuable insights. But it can be a lot difficult to manually analyze unstructured data. So, businesses actively seek new technologies and solutions to give them an edge over their competitors. Although, the last decade alone has shown rapid advancements in technology in every industry. This makes it difficult to decide which technologies to adopt to grow your brand. One such technology that’s in question today is, ‘text analysis’.
What is Text Analysis?
Text analysis, also known as text mining, is the process of compiling, analyzing, and extracting valuable insights or information from large volumes of unstructured texts, using machine learning and NLP (natural language processing) techniques.
The sheer volume of data available on the internet today is incomprehensible. And manually analyzing this data is not really an efficient option. To help you better understand the situation, let’s look at some numbers.
In 2014, there were over 2.4 billion internet users either consuming or generating content.
The number grew to 3.4 billion internet users by 2016. The coming year, 2017, added another 300 million internet users.
2017 also began the great information boom. Over 90% of information on the internet was created in or after 2017 and is mostly in textual format.
The year 2020 recorded a total of 4.66 billion internet users.
There sure is a heck of a lot of information on the internet, and finding information sources relevant to you or your organization, can be challenging.
Text analysis tools can compile and analyze large volumes of unstructured text and provide game-changing insights to help you grow your brand.
Text Analysis Process
The text analysis process involves a series of actions using various machine learning and NLP techniques to extract valuable information and insights.
Unstructured text processing:
The unstructured text data pre-processing involves,
1. Text Cleanup
The text cleanup process involves getting rid of any unwanted information from the compiled textual data. The process removes information such as ads from webpages, unwanted symbols, or standardizing text converted from the binary format.
This process involves splitting the text into white spaces.
For example: Just do it. The sentence results in 3 tokens, ‘just-do-it’. Each word is a token.
Simply put, it breaks raw sentences into words or sentences known as tokens that help you understand the context and interpret the meaning by analyzing the sequence of the words.
3. Part-of-speech (POS) tagging
The POS tagging involves labeling every word in a sentence with the right part of speech. The part of speech includes verbs, nouns, adverbs, adjectives, pronouns, conjunction, etc.
Text analysis techniques
Now that you know how text analysis processes unstructured data, let’s discuss some text analysis techniques used to mine specific data and insights.
Topic labeling is a text mining technique that can help you categorize and interpret large volumes of textual data based on the theme of the information source. You can identify and label popular topics of discussions revolving around your brand, organization, or industry. Labeling topics can help the tool identify and classify all information sources related to the pre-defined labels.
This technique is usually used to analyze large volumes of textual data, therefore increasing efficiency and productivity.
Sentiment analysis, also known as opinion mining, is a text mining model that can analyze and interpret the sentiments expressed by the author in any piece of text.
Brands and business organizations often use sentiment to analyze the public opinion of the brand. They do it by analyzing reviews and feedback from multiple sources. The analysis model then classifies the customer feedback into positive, negative, and neutral sentiments. This helps brands gauge their brand reputation. Also, further analyzing the negative feedback can direct you towards areas of focused improvement.
Feature extraction is a text mining model that specializes in extracting important features or product facets from large volumes of unstructured text data.
It can be a really handy tool for performing research or product development. You first compile data related to your industry and then use feature extraction to analyze the features of competitive products. You can easily access a comprehensive overview of all competitive products and make data-driven decisions to develop products with the right set of features.
Intent detection is a machine learning and natural language processing technique that can help you automate the classification of text based on intent. For example, the intent could be a simple inquiry, purchase intent, complaint, etc.
This text mining technique is widely used to enhance customer support experiences. The incoming customer support queries are automatically classified and directed to the right customer support agent. This helps in improving the customer support response rate as well as retention rate.
Similarly, you can also use it to analyze the efficiency of your customer support teams. You can analyze customer reviews of various support teams based on let’s say location. You can easily identify poorly performing teams and areas of improvement.
Intent detection can also identify recurring customer support queries. It can help you prepare your support teams for all possible scenarios and thus enhance the overall customer support experience.
Semantic similarity is a text analysis technique that analyzes the likeliness of two pieces of text having the same or similar meaning. One of the widely known applications of semantic similarity is Amazon’s product recommendation engines. It recommends similar products or products that are often bought together.
The entity extraction text mining model enables you to extract entities from any piece of text. The named entities are then classified as per pre-defined categories such as people, places, brands, monetary values, medical codes, and much more.
You can easily extract key features from big data which reveals what kind of information it contains and who is it related to. It can simplify the job of analysts, researchers, journalists, etc, that have to analyze large volumes of data and increase productivity.
Industry applications of text analysis
Ok, now that you know about the various machine learning data extraction techniques, let’s discuss some of the popular text analysis industry applications.
Social Media Management
Over 80% of brands and B2B marketers use social media platforms to promote their brands. But, if you have customers on social media platforms, you must analyze what they are talking about your brand. Text analysis voice of customers solution can help you analyze and understand the opinions, reviews, complaints, etc, related to your brand.
It can help you find key issues and complaints about your product or service. You can easily identify areas that need improvement and spot unsatisfied customers. It can help you offer a solution to unsatisfied customers before it late. Even engaging with the customer rather than providing an immediate solution can sometimes make a difference and increase customer retention rate.
Customer support is another area that is being highly influenced by text mining. Customer support teams get numerous queries, complaints, inquiries, etc, regularly. Intent detection, a text analysis technique helps classify these incoming tickets in real-time based on the intent of the author.
Let’s say, a customer reaches out to your customer support team to enquire about a product that he is interested in purchasing. Intent detection can easily classify this as purchase intent and direct it to the right sales executive. This can help you significantly decrease your response time.
It can also help you identify recurring issues and prepare your support teams for all scenarios.
Analyzing large volumes of data is an irreplaceable aspect of gathering business intelligence. Numbers can be easy to analyze, but manually analyzing text data can be too time-consuming and inefficient.
Over 80% of data on the internet is available in text. Also, most of any business’s data is available in text.
You can leverage various text mining techniques to extract the specific type of data or business intelligence with ease.
Similarly, you can also analyze data related to your competitors to understand their marketing moves and to dissect their strategies. You can even analyze what the people in the industry are talking about you or your competitors. Gathering business intelligence for data-driven decision-making has never been this easy before.
Journalism is another sector that witnessing wide-scale adoption of text mining techniques for data analysis. Journalists have to verify the legitimacy of any news before deciding to publish it. To do so, they have to analyze a heck ton of information and understand all facets of a story. But the process can be time-consuming if done manually.
You may also lose the chance to be the first agency to report the news which can decrease engagement or views. This is where text analysis software solutions can help you. It can analyze large volumes of textual data with ease. The various text analysis techniques can help you extract the specific information you need.
For example, Journalists can use the entity extraction model to extract information like names, places, dates, time, currency, etc, from large volumes of text data. This can help them identify key elements of any news. Similarly, they can also use keyword extraction to identify all news or information sources related to the case. Text analysis tools can help you automate the process of analyzing text data and increase efficiency.
Doctors often keep notes of clinical data. These notes account for approximately 80% of all clinical data produced. However, this is not part of the EHR (Electronic Health Record). All this valuable research data is locked in physicians’ notes. But the increasing applications of IoT (Internet of Things) have opened new possibilities. Now, doctors and physicians can use speech-to-text devices to keep an electronic record of all cases. This data can be later used as and when needed.
Doctors can identify the symptoms and use those to identify similar cases. This can help you easily identify the disease and recommend a suitable diagnosis. Text analysis tools can drastically decrease the time of examining a patient and recommending the treatment.
Apart from examining patients, hospitals also have to process a lot of documents and patients’ reports daily. Examining and sorting these documents can be tedious. Text mining techniques can examine and classify these documents as per pre-defined categories for easy accessibility. Many pharmaceutical companies have already integrated text analysis with their system to sort and examine research data.
Text analysis tools can benefit a lot of industries, and the financial sector is one of them. The financial sector involves banking, credit, insurance, trading markets, mergers, acquisitions, and much more. These activities produce a lot of unstructured data that is difficult to even sort, let alone examine. Also, examining all of this data is vital to carry out daily operations.
Text mining techniques can help you analyze and extract actionable insights from complex unstructured text data compiled from multiple sources. The various text analysis models can extract specific information when required.
Best Text Analysis Tools 2021
Now that you know the benefits of text analysis, let’s look at some of the best text analysis software solutions available in 2021.
Ta-daaaaa, it’s our very own text analysis tool, BytesView.
I know you’re pissed, but hear me out.
After all, you have come this far!
BytesView is an advanced machine learning and NLP-based text analysis tool. It can compile and analyze large volumes of text data from multiple information sources with ease. The various text mining and analysis models can help you analyze and extract valuable insights from unstructured text. BytesView also offers API services that can help you train custom data analysis models with data specific to your organization to increase accuracy and efficiency. The dedicated plugins make it much easier to integrate unstructured text with our system for analysis.
Key features of BytesView,
- Dedicated plugins for integrating the data
- API access to integrate it with your system
- Various text mining models to analyze data
- Train custom data analysis models
- Analyze large volumes of unstructured text
- Gather business intelligence with ease
MonkeyLearn is another great text analysis tool to analyze unstructured text. It is a popular tool that is widely used to analyze customer support data. Although, the tool does provide various solutions for a wide range of industries. The text-mining models include sentiment analysis, intent detection, keyword extraction, and more.
Key features of MonkeyLearn,
- Comprehensive customer support solutions
- Various text analysis models to analyze customer support data
- Automate ticket tagging of customer support queries
- Various plugins for easy integration of data
Lexalytics is one of the most comprehensive text mining tools out there. It can analyze complex unstructured textual data and transform it into business intelligence. The tool offers various machine learning and NLP models to analyze textual data. You can also access its API and integrate it with your system. It can help you train text analysis models with data specific to your organization.
Key features of Lexalytics,
- Integrate and analyze unstructured text data
- Various text mining models to dissect and interpret data
- Train custom text analysis models
- Solutions for a wide range of industries
The article aims at providing all the necessary information to help you understand text analysis. Analyzing data is now an important aspect of any organization’s day-to-day activities. Text mining tools can help you automate the analysis of large volumes of text data. This can drastically increase efficiency and help you make data-driven decisions. We hope you consider BytesView to explore the possibilities of analyzing textual data.
If I missed anything important, let me know in the comments.
Until next time!