Text analysis tools attempt to bring insights to the 80% of online content that’s words (termed unstructured data) rather than numbers. Some experts even put this number higher, arguing that 90% of all data is unstructured. What if a tool can help you make intelligent business decisions by utilizing this unstructured data systematically to uncover customer insights? Today, we review the best text analytics tools of 2022.
What is text analytics?
According to Wikipedia, text analytics or text data mining is:
Text mining also referred to as text data mining, similar to text analytics, is the process of deriving high-quality information from text. It involves “the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources.
Why, you might ask, do we care about analyzing text? The answer is two-fold. First, according to IBM, 80% of all data is in the form of text. We have robust tools for analyzing numeric data, build over the millennium. For instance, we likely all learned at least some rudimentary tools for analyzing quantitative data, such as mean, median, and mode. Some of us learned more advanced analytical tools for quantitative data such as regression, ANOVA, and other statistical analysis strategies to understand the world around us.
You might have heard that a key to creating brand awareness is achieving customer engagement and satisfaction. You also might recall all those times when you struggled to figure out the actual intent of a customer’s feedback. Especially in sectors like hospitality, financial services, PR and advertising, and retail, customer feedback is essential for building rewarding relationships with consumers by delivering value. That means understanding their evaluations of your brand as well as those offered by the competition and how they make trade-offs between products like yours. For instance, consumers don’t just choose which movie to watch, they often must choose between going to a movie versus going to a sporting event or other type of entertainment that fulfills the same general need. They may even face a situation where they must make trade-offs between buying groceries and healthcare, two totally unrelated categories of purchases when they have limited disposable income. In such cases, text analysis is critical for decision-making.
You can’t just use text as input for these powerful statistical programs because the data isn’t in the right format for analysis by tools like SPSS and SAS. Instead, we’re forced to code the data; transforming text data into numerical data. Sometimes, that’s OK. For instance, if we want to understand churn data, we can provide common options that capture the consumer’s rationale for cancellation by assigning a numeric code to each option. The analysis then proceeds in much the same way as other numeric data, although, since this data is not ratio, interval, or ordinal data where the number has a relationship to other numbers (such that bigger numbers translate into more), there are limitations in the types of analysis we can do with this data, termed nominal data. Unfortunately, coding text data in this way often means we lose the richness offered by text data. For instance, coding data reflecting how a consumer feels about your brand often ask consumers to translate those feelings on a 1-7 scale. If you’ve ever been asked by a doctor to rate your pain on a scale of 1-10, you know that translation of emotions into numeric data has little meaning as each person has a different pain threshold and experience with pain.
How do the best text analysis tools work
Most of the best text analysis tools attempt to draw insights from the text while retaining the richness of the data. Given our statement above that 80% of all data comes in the form of text, it’s easy to see why using these tools provides great insight to brands that helps them optimize their performance with better decision-making. They do this by using AI (artificial intelligence) combined with ML (machine learning) into something called NLP (natural language processing), rather than attempting to reduce information richness by coding the text data.
AI is not leaving us anytime soon. In fact, with time, it is expanding its presence across various sectors including business. With machine learning, you can actually solve business problems. But, you might think, how exactly can it help your business?
A text analysis guide can help you better understand how these tools help you improve customer service and provide a better customer experience, as well as tailor products and other marketing elements to the needs of your target market through using machine learning algorithms and natural processing language. It helps you understand how your customers construct their world and make decisions.
This article provides eight of the best text analysis tools you can use to understand your market and make better decisions to notch customer engagement a level up.
8 of the best text analysis tools for businesses
This tool helps you turn your qualitative data (text data) into actions. For instance, you send a survey or a feedback form to your customers (free-text feedback) and now you have a lot of qualitative data to analyze.
Thematic analysis lets you interpret this data more easily by sorting them and categorizing data based on common themes found in the feedback. With this tool, you can analyze the data, create a meaningful data report, and also glean insights necessary to take appropriate actions post-analysis.
With a single click, you can connect your feedback from online reviews, chat, surveys, social media comments, and more. Hence, you can send out as many open-ended questions as you want as well as derive insights from engagement with your social media communities.
This tool provides you with ready-to-use sentiment analysis tools. Sentiment analysis tools basically help you understand the tone, intent, and track the change in sentiment over time using customer feedback. This tool allows you to assess the performance resulting from your communication efforts and compare your performance with the competition.
MonkeyLearn helps you identify changes in the way your market responds positively or negatively to your business and provides insights to help you optimize your market’s sentiment toward your brand.
You want your customers to keep coming back to buy from you over time. This happens when you create brand awareness that resonates with your target market. Chattermill helps you improve the customer experience for your brand and also improve brand advocacy through creating satisfied and engaged customers. This tool helps you in the following ways to achieve that goal:
- Language Identification or language detection
- Topic Analysis
- Intent Tracking
- Data Extraction
- Sentiment Tracking
Chattermill provides customer feedback analysis in seconds and helps you make decisions based on these metrics of customer satisfaction.
4. Google Cloud NLP
You can find and label fields in documents such as chats, emails, and social media posts with this software through entity analysis. This tool also helps you understand customer opinions via sentiment analysis.
Besides insight extraction, the app also offers features like analyzing and storing text. It can also locate domain-specific entities within documents using custom entity extraction.
5. Relative Insight
A customer’s feedback reveals his/her thoughts, feelings, and desires. This tool accurately captures these feedback elements and generates insights from social media data, reviews, and other qualitative data. This data helps you understand your customers better and offers suggestions to improve your business strategy.
This tool helps you in multiple ways. It helps you with finding the right words while hiring top talent and its sentiment analysis helps you understand your customer better. You can make changes to your brand awareness campaigns and content creation with the help of this tool’s analysis.
6. Amazon Comprehend
This tool helps you unleash the hidden insights and relationships in your unstructured data. It identifies the following things to make decision-making easier for you:
- Language of the text
- Extracts key phrases
- Automatically organizes files by categories
You can also analyze documents, product reviews, and social platforms.
7. IBM Watson studio
IBM Watson is a supercomputer used for any number of qualitative tasks, including winning a game of Jeopardy. guides data exploration, automates predictive analytics, and enables effortless dashboard and infographic creation. You can get answers and new insights to make confident decisions in minutes, all on your own.
Analyze text to extract metadata from content such as concepts, entities, keywords, categories, sentiment, emotion, relations, and semantic roles using natural language understanding.
This tool is also used to enrich customer engagement by giving you insights into the meaning of customer data in form of feedback and survey replies. It proactively surfaces the hidden signals and meanings behind those long customer texts. It essentially takes the burden off of manual analytics. This saves time and increases efficiency.
Wrapping up the best text analysis tools for 2022
This is all for text analysis tools. These tools are a cost-effective solution to analyze large volumes of qualitative data from your customers. Plus, the machine learning algorithms ensure accuracy in analytics.
So, there is no need to invest in a team of machine learning experts since these tools can give you results in minutes. The article gives you a list of eight tools that you can use. However, it is up to you to decide which one works for you based on their features, prices, and your business needs.
Parita Pandya is an Engineer turned Writer. She usually finds herself writing for businesses. When she is not writing, she is either strumming her guitar or penning her thoughts down on paritapandya.com.
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