It is tiresome and time-consuming when working with large amounts of data manually.For example, a company or product reviews and customer feedback. That is why developers came up with application tools that use artificial intelligence to analyze data to come up with a clear presentation of data, either in the form of charts or graphs.
How to use AI in excel for automated text analysis
Excel data visualization for automated text analysis is widely used to analyze all this data automatically in a spreadsheet. Data from all company sources are categorized as either positive or negative feedback or reviews. With this kind of data, you will know about various parts to improve on as a company owner.
In this guide, we will look at how we can use AI in excel for automated text analysis.
What we shall cover in this blog,
- What is text analysis
- how do we use AI in text analysis
- How we benefit from using text analysis
- Which are the various types of text analysis techniques
What is text analysis?
Text analysis, also known as text mining, is the use of a computer system to read and extract data from written text. The data can be from company product insights. This data is sorted, classified and represented according to relationships and sentiments.
In a practical example where a company gets reviews and customer feedback through emails and on services websites such as App Academy school reviews, text analysis can be used to categorize positive and negative reviews.
Below graph is an example of text representation by use of AI in excel for automated text analysis.
How to use AI in text analysis
AI techniques like machine learning and natural language processing enable text analysis tools. These application tools automatically process and detect words and categorize them.
Machine learning algorithms are used in text classification. The widely used AI applications are Support Vector Machines (SVM), Naïve Bayes(NB) family of algorithms, and deep learning algorithms. Mostly these algorithms discover insights, patterns, and relationships that a normal analyst would not perform.
How we benefit from using text analysis
Considering the size of data from emails, tweets, chats, product reviews, and support tickets you receive, it is hard to tell what topics are discussed on all these platforms.
To follow up with the data, you need to analyze it in a spreadsheet and get insight. This is time-consuming when working manually as an analyst.
When we use AI in excel for automated text analysis, it becomes easier to get data trends in a go. These are some of the benefits of using excel for automated text analysis:
- It is scalable
No matter the data type, you can run text analysis tools to get insight within minutes. It is easy to scale up or down these tools without worrying about how long it will take.
- Text analysis in real time
It detects issues automatically in real time, so it helps you to act on issues right away before they escalate.
- Consistency and accurate
While using Excel for automated text analysis, data is accurately formatted. Unlike humans, these AI tools work on how they are trained, making them Accurate and consistent.
Now we look at various classifications of text analysis in excel.
Types of text analysis in excel
Text can be tagged in pieces by category, depending on its content. Text classification can take many forms; you can decide to identify the topic in a given set of data, its sentiment or its language.
Topic analysis refers to the classification of text based on topic. This is helpful when you want to reveal topics that the most mentioned feedback you receive from customers. When we use AI in excel for automated text analysis, text classification in your spreadsheets will give an insight into what your customers want and talk about your product.
Using an example where the response is “User-friendly interface, I like it,” the text would be tagged as user experience. In contrast, feedback from a survey, “Your team is awesome,” would be tagged as customer support.
Sentimental analysis and intent detection
While using excel for automated text analysis, it can automatically assign sentiment tags by using sentiment analysis.
This analysis helps you to get an idea from your data whether the customer is praising a particular product (positive feedback) or criticizing it (negative feedback).
This graph shows an example of data analyzed using sentiment analysis.
Intent detection is a tool used in excel for automated text to detect what the customer needs. This is a very powerful tool; it helps you to improve your products and services according to your customer’s satisfaction.
For example, email feedback can be classified as interested or not. Consider email feedback, “Looks nice. I would like to know how it works.” When we use AI in excel for automated text analysis, this piece shows the customers are interested in the product. Thus, it can be categorized as Interested.
Now that we are familiar with text analysis definitions and techniques, we shall look at how we can implement this knowledge in excel in chartExpo.
Steps on how to use AI to excel in automated text in chartExpo.
To use chartExpo to visualize data, you have to install it in your google spreadsheets as an add-on,
Step 1. Choose a working model
When you sign up in chartExpo, you will see a variety of models: sentiment analysis and other chart categories.
Step 2. Working on sentimental analysis model
Filter the available sentimental analysis models which you would like to use.
Step 3. Create or select your Spreadsheet
Step 4. Create chart
By using the Create chart button provided. After a few seconds, the chart is automatically generated from your provided data.
This is one of the models of excel for automated text analysis. There are many other text analysis models offered on this platform. The choice of each model is determined by the type of insight you want to get from your data. You can also specify the topics which will be analyzed.
Excel and google spreadsheets use AI in excel for automated text analysis to eliminate the tiresome job of manually analyzing data. It has helped to decide on actions to take according to the data. This encourages you to improve on that area of study ahead of the game.