Text Analysis – Everything You Need to Know!

Text Analysis - Everything You Need to Know!

Text Analysis Tool

There is so much data floating around, especially in the digital space. Organizations receive a lot of information from feedback, social media posts, product reviews, and support tickets, to name a few.

When you are dealing with open ended feedback, you need a way to sift through the information. Yet, it is imperative that you do so, so as to get all the relevant insights you can use to improve your business.

With over 80% of data occurring in an unstructured format, you need to organize information in digestible bits. You can only do this by applying text analysis techniques using the right software. While it is possible to do it manually, it could be time-consuming and require a lot of human resources.

Our article will go into an in-depth exploration of text analysis and how you can use it to improve business operations.

Exploring the Meaning of Text Analysis

What is Text Analysis

Imagine the amount of information available on the online platform. Experts estimate that as of 2020, there should be about 40 trillion gigabytes of data. 95% of businesses have no idea how to use unstructured data.

Fortunately, there is some good news with text analysis software. Reports show that by the end of 2020, a significant portion of analysis will be fully automated. To fully take advantage of the insights and wealth of information available in unstructured data, organizations need to adapt text analysis techniques.

To give an analogy of text analysis, imagine receiving a bag full of things you could find use for. You will take a bit of the time to sift through the bag’s contents and separate the items into specific categories using some parameters. You could base it on functionality, size, color, or even price.

Now, apply the same thinking to large amounts of text within a document or sentence. You need to use the right techniques to pick out what is relevant to you. You must use a kind of filtration process or specific techniques to achieve your goals.

If you have too much information, you may not be able to complete the task manually. Humans are prone to errors arising from boredom, inattention, or inability to understand the data. Using the right text analysis software, which incorporates artificial intelligence like machine learning or natural language processing, makes it easier for you.

You will find instances where text analysis and text mining are used interchangeably. There is not much difference between the two because both work towards deriving quality information from unstructured data.

Text Analysis Techniques

Text Analysis Techniques

Text analysis techniques incorporate the use of different tools. Such tools include data mining, statistics, machine learning, information retrieval, and computational linguistics. It is a multidisciplinary field that may be challenging if you do not have the right knowledge and software to help you with the process.

You must follow specific steps when analyzing text. We can summarize them as:-

– The gathering of data from multiple sources. Such sources include emails, customer feedback from surveys, blogs, plain text, product reviews, and web pages, to name a few.

– Data pre-processing and cleaning to identify and remove any anomalies. The process allows for the extraction and retention of valuable information that you may not be seeing within your data. There are specific applications and tools you need to complete this step.

– Structuring of data by converting the information you need from the unstructured data.

– Pattern analysis through classifications or categorizations
Storage of the data you have structured in secure databases

Let’s explore the steps above in a little more detail while highlighting the text analysis techniques.

1. Extraction of Information
When you receive a chunk of text or data, you must find meaningful information within. The extraction of information requires you to look at the different attributes and entities and their relationship.
Once you access the information and extract it, you must pass it through relevant checks to determine its relevance and efficacy.

2. Retrieval of Information
Information retrieval looks at patterns using specific parameters such as phrases or set words. The algorithms will help monitor and track end-user behavior to come up with the relevant information.
Search engines like Yahoo and Google use information retrieval to give you the right results every time you type in a search query.

3. Categorization
Categorization takes everyday language and assigns it to topics depending on their content. Think about it more like natural language processing (NLP) or supervised learning, which gathers, processes, and analyzes text documents.
It does it by revealing indexes or topics within each page, sentence, or sub sentence. You may, for example, find its use in hierarchical definitions, personalized commercials, spam filtering, amongst others.

4. Clustering
Clustering will put information into clusters or subgroups by identifying intrinsic structures in the data. The process comes with its own challenges because you do not have any prior information on the unlabeled textual data.
It makes it difficult to form any meaningful clusters. However, it is a critical step when processing your data, especially if you want to use other algorithms to analyze your text.

5. Text Summarization
The text you finally generate from the text analysis must make sense to the end-user. As the name suggests, summarization brings all the information together into digestible chunks.
It allows for the presentation of the findings without changing the original document’s intent or meaning. You will find a lot of text categorization techniques in summarization, such as neural networks, decision trees, swarm intelligence, and regression models.

6. Sentiment Analysis
Sentiment analysis gives you more in-depth information about what your customers feel about your brand or product. It looks at emotions, feelings, or emotional polarity such as positive, negative, or neutral.

Improving VOC with Text Analysis Software

Text Analysis Software

You cannot operate like an ostrich in the business environment. Burying your head in the sand and not wanting to hear what your customers are saying is a recipe for disaster.

Voice of the customer (VOC) requires you to gather, understand, and analyze customer feedback. Only then can you improve customer service resulting in higher customer retention.

Sending out questionnaires or surveys or getting data from email, telephone communication, social media, and ticketing are just the first steps. Analyzing the feedback you receive will allow you to peel back the layers and truly listen to your customers with an inner ear.

You also get to see what your competitors are doing by gaining access to the trends that attract or repel customers.

Text analysis software saves you time and money, and you can analyze, measure, and translate the feedback into actionable insights. You process large volumes of data in real-time, allowing you to respond in a timely manner to customer concerns. Most importantly, you base any decisions you make on the insights you gather from the data.

Tatvam Has the Solution for Analyzing Text and Sentiments.

Text analysis can be challenging, and most organizations struggle with it. With the constant and continuous barrage of information coming at them, they cannot analyze the data efficiently.

While automation has made it easier to complete some of the tasks, not all organizations can afford them. Artificial intelligence programs require a resource investment that smaller companies may not be able to afford.

That is why you need a reliable partner who understands the challenges and has the solutions for you. You get all that and so much more when you partner with the Tatvam. Our range of services includes text analysis services covering areas such as sentiment analysis and voice of the customer.

Sentiment analysis brings to light things that the customers may not be directly saying. We look at attitudes and opinions to show whether your customers are happy or unhappy with the services. With such information, you can prevent a crisis by being proactive rather than reactive.

We have over the years helped our clients analyze the voice of customers resulting in an improvement in customer service. We gather information from all the channels the clients are likely to be on. Such channels include social media, online reviews, email support logs, survey responses, amongst others.

We then aggregate all the information we collect in one place for easier access. Using our text analysis software, we go into in-depth information retrieval and exploration about what your customers are trying to say to you. Everything we do is actionable and measurable.

You will start to see the results of investing in text analysis with higher customer retention.

Final Thoughts

Every business must do all it can to survive in a competitive environment. Your business depends on your customers, and the chances of success increase significantly if they are happy.

To truly understand what your customers are saying or thinking about your brand or product, you need data.

Every day, you receive and generate so much data that may just end up sitting in storage. Yet within the text or pages, they could be insights that could elevate your business to the next level.

Take advantage of text analysis software and techniques such as those you will find at Tatvam.