Understanding text analysis tools
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For many companies, text analysis tools are at the heart of understanding their business, product, and customers. But why analyze text? Because as we can see below, people are writing more than ever before:
- Twitter boasts more than 300 million monthly active users and more than 500 million tweets per day.
- 281 billion emails were sent and received in 2018. This number is also expected to increase each year.
- More than 4 million blog posts are published online every day as of 2018.
- 45% of consumers go to social media with questions and issues.
This text data is very valuable. Analyzing it means understanding how people feel and talk about our company and products. It means discovering trends, deepening our understanding of feedback, and uncovering new insights.
However, text data analysis requires time and manpower. It requires large amounts of data and data scientists to analyze it. So, many are investing in the development of AI text analysis tools to streamline the process. By tagging a corpus of text data – such as related tweets or customer feedback – we can begin training a system to analyze our data for us. In doing so, we open the door to a multitude of benefits: improved customer support, real-time feedback analysis, higher levels of analytical accuracy, and more.
Below, we've listed common types of text analysis, their uses, and their benefits. We hope it helps you refine your needs and find the best text analysis tool for your project.
Types of text analysis
Word frequency: This is a basic type of text analysis that counts the number of times a word appears across texts. It's an effective way to discover new keywords, particularly when analyzing customer feedback on social media.
Sentiment analysis: A popular use of AI text analysis, sentiment analysis is the process of analyzing keywords and phrases in a text to determine whether it is positive, negative, or neutral. This is helpful for analyzing social media posts and drawing trends from customer feedback.
Text classification: In simple terms, text classification is the act of understanding what a given text is about. By analyzing a body of text and recognizing its keywords and/or intent and sentiment, a machine can classify it under a predetermined set of categories. For example, you can collect and classify customer feedback about a product under categories like features, pricing, improvements, and complaints.
Language detection: Language detection is the process of classifying a text according to its language. This is often used by international businesses who may want to ensure that customer support requests are automatically forwarded to the most appropriate teams.
Intent variation: In natural language processing and text data analysis, “intent” refers to the purpose of a user’s input. For our purposes, think asking a chatbot or search engine when its business hours are. Intent variation is important because people don’t always express the same thing the same way. For example, a query like “What are your business hours”, could also be written as “What are your hours of operation,” or “What time do you open and close.” Capturing intent as it relates to your field of work is especially useful for training chatbots and improving customer service.
Keyword extraction: As the name implies, this is the process of extracting keywords to summarize a text. Keyword extraction is a popular use for text analysis tools because it can reveal search behavior and/or popular terms related to services or products. It also allows for data visualizations like idea clouds to improve data analysis.
Feature extraction: In a text analysis tool, feature extraction allows for the identification of specific characteristics within a text. If you’re analyzing car descriptions, for example, features might include brand, model, year, etc.
Text clustering: As the name suggests, text clustering puts similar text data or documents in groups, or clusters. This kind of text data analysis allows for the discovery of natural clusters in your data, which in turn means discovering patterns and trends. Text clustering is helpful for tasks like analyzing customer support issues for pain points.
Named entity ecognition: NER is the process of locating and labeling named entities within a piece of text data. These entities are most commonly people, organizations, products, and locations, but can vary depending on the project. NER is useful for understanding the structure and meaning behind a piece of text, and is a common tool for improving search algorithms.
Named entity linking: Entity linking is the act of locating and disambiguating entities via a knowledge base. The purpose of this is to add metadata to named entities. This means identifying the appearance of a named entity, and which specific entity it is. For example, NER might be able to locate Tim Cook as a person, but NEL can identify mentions of Tim Cook, the CEO of Apple, by linking each use of the name to a specific knowledge base. NEL allows for greater text analysis, more accurate search results, and improved customer service.
Summary extraction: Also known as text summarization, summary extraction condenses a text into a comprehensive synopsis. This means creating a summary from keyphrases in a document, or by generating new sentences based on an understanding of the meaning behind a text. In both cases, machine learning algorithms need to understand the language and message in each text. This is helpful for newsletter generation, analyzing a large number of company documents, and streamlining internal workflow.
How to start
Developing and implementing an AI text analysis tool starts with defining the goals of your project. Are you looking for content recommendation solutions? Analyzing customer feedback? Improving your chatbot or in-site search engine? Knowing this will give you a starting point. From here, you can define which types of text data analysis best suit your project.
Once you've defined your goals, your next priority is high-quality data. To ensure your results meet your expectations, and to ensure your project meets its goals, you’ll need the appropriate amount of relevant text data. This can come from in-house emails and feedback forms, or it can be scraped from online sources like Twitter. This data needs to be cleaned to avoid error, and then the data annotation process can begin.
Data annotation is the task of labeling data for the purposes of training your machine learning model. In sentiment analysis, for example, you might label texts as positive, negative, and neutral. In named entity recognition, you might label texts for the appearance of people, places, and dates. The exact annotation type will vary depending on project needs, but ensuring quality annotation is an indispensable part of ensuring your text analysis model is successful.
If you’re not sure where to start with AI text analysis but want to learn more, get in touch!