What are Text Analysis Techniques?

What are Text Analysis Techniques?

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Here are some interesting statistics to get your mind warmed up. We are churning out up to 2.5 quintillion data bites every single day. 95% of businesses have no idea how to manage unstructured data. By the end of 2022, you could expect as much as 93% of unstructured data on the online space. ?

Imagine trying to understand all the data available manually. Calls for a shiver moment, doesn’t it? Not to worry, text analysis software and techniques provide an easy way to manage all these data. By 2020, data management and analysis may entirely depend on automation and AI.

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What Is Text Analysis?

The simplest definition of text analysis is any process you undertake to mine information from unstructured data. You do it with the aim of uncovering information or insights that you can use to make decisions.
If, for example, you are doing a customer survey, you must analyze the data you receive to discover the hidden trends and themes. Only then can you implement customer services that will improve the overall experience when they are using your brand or product.
Text analysis is a complicated process, yet one that is critical for most organizations. To create greater efficiency, it is important to have the right software for text analysis. With the right text analysis techniques, you:

    ?Gain valuable insights that you can use to improve the customer experience whether from a whole document, single sentence, or sub sentences
    ? Get to save time and money that you would otherwise spend going through tons of information. Concentrate on your core business while letting the text analysis software mine the information for you
    ? Benefit from real-time analysis, allowing you to act faster or more promptly any time your customers raise specific issues. With the right training modules, you can capture customer sentiments from tweets, tickets, emails, videos, and reviews.
    ?Avoid human error that may arise from manually trying to sift through unstructured data. The use of algorithms for analytics results in better understanding and greater accuracy than a human being would be able to achieve.

Text Analysis Techniques You Need To Be Aware Of

A typical company receives a lot of information from both internal and external sources. Such include chats, emails, support tickets, surveys, and customer queries and feedback. Externally, the data comes from sources like social media, online reviews, and news reports, to name a few. ?

You cannot afford to ignore this information because you need insights to implement relevant changes and processes in your company. However, imagine trying to sift through the tons of information you receive manually.? ?

Unless you have superhuman capabilities, it may be an exercise in futility. You will spend significant time and need to hire sufficient staff to handle the work. The impact on the bottom line for your company would most probably be unmanageable. ?

The right text analysis software and text analysis techniques help you complete the job efficiently. You also get to eliminate the guesswork and mistakes that may arise from human error. ?

Machine learning can significantly ease the process of analyzing, understanding, and deriving meaning from unstructured data. The use of algorithms will help with establishing connections within the data you have. You eliminate the need for trying to establish predictions on your own. ?

So what text analysis techniques do you have available? Let’s dive into it.

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1. Information Extraction

Data analysis follows specific steps, as we will show below.

When you are extracting information, the main aim is to turn your unstructured or semi-structured text into a database that has structure. You must first evaluate the data you have available and identify key phrases, entities, or speech paths.? ?

You will then look for predefined sequences through matching patterns. The outcome will be the establishment of a relationship between attributes and entities. You may, for example, want to extract information such as company names, keywords, product specifications, or even prices. ?

2. Text Categorization

When dealing with unstructured text within a document, it is important to assign categories. You will need to process the data, index it, apply dimensional reduction techniques, and eventually classify it. ?

You have several statistical classification methods available, including decision trees, nearest neighbor classifiers, and naive Bayesian classifiers. With the right text analysis software, text categorization becomes easy.? ?

You do not need to create any rules or patterns manually. All you need to do is provide examples, and the software will do the rest for you. For example, if you are trying to look at your customer service, certain words could point you in the right direction.? Customer feedback, including words like patient, helpful, and informative, can yield the desired insights. ?

You must have the right training data to truly appreciate the amount of information you can get with text categorization. When dealing with many categories bearing certain similarities, you must invest in the right amount of training data.? Only then can the algorithms truly differentiate the information and yield relevant insights.

3. Clustering Similar Content

Text analysis techniques should work at creating classes for multiple groups within the documents. Whatever you have in a cluster must share similarities, quite different from what exists in another cluster. You do not have access to predefined categories to use as a reference. ?

Think about every time you type a search query into the search engines. Within a short time, you will have the results showing up on your screen. The search engines build clusters that they obtain from different web pages using different algorithms. Breaking down unstructured data uses combinations of letters or adjacent words to give you the results you desire. ?

4. Text Classification

Text classification as an analysis technique requires you to assign categories or predefined tags to the unstructured data. You can tackle a wide range of information resulting in better data that you can use to implement changes or tackle problems within the organization. ?

There are different components to text analysis, including:-

  • Sentiment analysis much like you would get from customer surveys. You get to understand how your customers feel about you, whether negatively, positively, or neutral. It is also an important tool when carrying out brand reputation surveys, market research, or product improvement planning.?
  • Topic analysis or modeling that looks at themes or subjects as classifications.
  • Intent detection gives you more insights into customer feedback, whether it is a complaint, compliment, or an intention to buy.

5. Word Frequency

Every text will have certain words or concepts that reoccur. Frequency text analysis techniques involve the use of term frequency inverse document frequency (TF-IDF). Suppose you are analyzing customer feedback to your services. You may find specific words that could give you a good indication of what the customers think of your offering or services. ?

Feedback from support tickets may show recurring words such as late or delivery over and over again. It may show that you will need to invest in improving your services when it comes to delivering goods.? ?

6. Data Visualization with Relevant Cues

Relevant visualization cues include things like colors, document categories, graphs, charts, and text flags.? It helps with creating distinctions between entities, phrases, and individual documents.?

The use of visualization allows you to create hierarchies within the structured data you are analyzing. It can also help you uncover trends that you may not have thought about before. ?

7. Summarization to Create Relevance to the End-User

Summarization requires you to identify the most critical parts of the documents you are working on. The steps are as follows.

? Preprocessing will require you to build a structured text representation.
? Processing incorporates the use of algorithms.
? Semantics technology helps you not to lose the meaning of the text when you summarize it.
? Development is where you finally obtain the summary.

Tatvam Can Help With Your Text Analysis

Text analysis will give you valuable insights that you can translate into effective business tools.? It will help you stand above the competition because you are using data to support every decision you make. ?

To reiterate our earlier point, text analysis is a complicated process requiring the right software. Putting together in-house resources to handle the work can be expensive and time-consuming. It may be difficult for a small company or one that is just taking off to do. ?

It may also take you away from your core business, forcing you to divert money into an area you could easily outsource. Yet text analysis is critical for your marketing and business growth strategies. ?

It is important that you find the right partner to help you with the process. Tatvam has all the solutions readily available for you. We have the relevant expertise and tools to transform unstructured data into information that you can use to scale a business to the next level. ?

Talk to us today and start the process of letting data work for you and for the good of the company. ?

Final Thoughts

Data analysis is a critical component for any company that hopes to make it in the business environment. It is an efficient, cost-effective way of collecting and analyzing customer insights.??

The right software for text analysis and the right techniques are crucial for your business. Finding the right partner to handle data analysis for you is critical if you cannot do it in-house. Let the professionals like those at Tatvam handle the task for you while you concentrate on your core business.?

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