Nowadays, manual data processing is becoming less and less feasible. We are surrounded by so much data that it is hardly possible to organize and analyze it properly without some automation.
Powered by AI, text analysis allows you to analyze large quantities of unstructured data in a fast and scalable manner. Here is everything you need to know about text analysis, including its most common techniques, uses, and limtations.
What is Text Analysis?
Text analysis is a process that involves automatic extraction and classification of meaningful information from unstructured text data. Businesses can apply text analysis techniques to make sense of large amounts of qualitative data and get actionable customer insights.
Text analysis can be performed on any qualitative data source such as online reviews, support tickets, social media content, emails, or surveys with open-ended questions. Through text analysis, businesses can identify key topics that arise in customer feedback as well as the sentiment surrounding each of those topics.
AI text analysis allows businesses to automate time-consuming processes and reduce manual work while also improving the quality of analysis and ensuring its accuracy. As a result, companies can quickly identify customer preferences and trends, make data-driven product decisions, and boost customer satisfaction.
Text Analysis Methods & Techniques
Text analysis uses a variety of techniques to make sense of unstructured data and extract meaningful information. Here are some of the most common text analysis methods and techniques.
Text classification is the process of categorizing text documents into different groups based on their content. It involves training a computer model to automatically recognize patterns in the text and assigning predefined tags to new content. Text classification is an extremely versatile and efficient way to structure large amounts of data. It can be applied to any type of text in any industry to reduce the workload and identify meaningful information.
The most common classification techniques are topic modeling and text sentiment analysis. Topic modeling is used to categorize texts into different topics or subjects, while sentiment analysis is applied to identify whether the sentiment behind the text is positive, negative, or neutral.
Consider this review:
“The charger broke in just 3 months, unbelievable!”
This review could fit into such categories as Charger or Product Lifetime. Through high-quality sentiment analysis, it would also be possible to determine that the review is negative.
Text extraction is another popular text analysis technique that extracts pieces of data from text. Businesses can choose to extract words and phrases that are relevant to them such as keywords, company names, product names, feature names, etc. This technique can be used in conjunction with other ones to categorize and extract important data simultaneously.
Word frequency leverages the numerical statistic TF-IDF (term frequency-inverse document frequency) to identify the words or phrases that occur most often in a piece of text. Word frequency allows you to determine which aspects of your business or product customers bring up the most in their feedback. With that understanding, you can discover your strengths and weaknesses and prioritize the most urgent areas for improvement that affect many of your clients.
For example, when analyzing support tickets you may find out that “packaging” is one of the most frequently used terms. That would be a signal to look into your packaging and utilize other text analysis methods to find out what your customers say about it and how you can fix the issue.
Concordance is a key technique for identifying the context of a text and analyzing the use of certain words and concepts. While analyzing customer reviews, you can use concordance to assign emotions or attitudes to particular attributes of your product. If you determine through word frequency analysis that your reviews often feature the word “frustrating”, you can use concordance to identify whether it’s the checkout or the delivery process that your customers find frustrating. Concordance is also helpful in clarifying vague or ambiguous wording by providing more context.
Collocation is used to identify pairs and groups of words that often occur together. This technique is helpful for analyzing terms that are expressed in two or more words, for example, “ease of use”, “customer service” or “delivery fee”. It can be used to identify bigrams (two adjacent words) or trigrams (three adjacent words). Collocation is a crucial tool for making the analysis more detailed and accurate since it allows one to focus on particular attributes of the product or service.
Clustering is an alternative to text classification that has one key difference. While classification includes tagging examples to train models, clustering algorithms do not require training data and instead function as unsupervised machine learning. That allows clustering algorithms to mine information faster, even though the analysis might be less accurate compared to text classification.
AI Text Analysis Benefits
Thanks to its ability to extract meaningful information from large sets of data, AI text analysis can help businesses improve customer experience and streamline product development.
Text analysis can help businesses get the most out of their customer reviews. Companies that struggle to close the customer feedback loop can leverage text analysis to turn unstructured data into actionable insights.
Thanks to sentiment analysis, you can quickly separate positive and negative reviews into two categories that require different approaches. In positive reviews, you should aim to identify your strengths so that you can keep performing well in those areas and increase the loyalty of your promoters. When analyzing negative reviews, you can identify particular touchpoints that need improvement and flag reviews with words that imply urgency.
In product development, text analysis can be used to identify and prioritize features requested by customers. Through analyzing customer feedback, businesses can extract information about specific features that customers mention the most. This information can guide product teams in making data-driven decisions about which features to prioritize in development.
Text analysis can also make your competitor analysis more accurate and effective. By understanding what customers like or dislike about your competitors’ products, businesses can identify gaps in the market or areas where their products can differentiate. This provides valuable insights for product positioning, unique feature opportunities, or marketing strategies.
Since text analysis is great at processing large data sets, businesses can utilize it to identify emerging trends, market demands, or changing customer preferences. With high-quality analysis of industry reports, market research, and social media discussions, you can adapt your product strategy to meet your customers' needs and stay ahead of the curve.
Machine Learning Text Analysis Challenges
While AI text analysis is very effective for processing qualitative data, it still faces some challenges.
Language is complex and often contains ambiguity, sarcasm, irony, or cultural nuances, especially when it comes to customer reviews that are written in informal language. Such language can be challenging for AI models to interpret. AI systems may struggle to grasp the intended meaning or context, leading to misinterpretations and inaccurate conclusions.
Training data bias
AI text analysis models can inherit biases from the data they are trained on. If the training data contains biased or unrepresentative samples, analysis results may end up skewed. Therefore, it’s important to ensure training data is diverse and representative.
Privacy and security
Text analysis often involves processing sensitive information such as personal data, financial details, or confidential business information. Maintaining the privacy and security of this data is crucial to prevent breaches, so businesses must have robust data protection measures in place.
The decision-making process of AI text analysis models can be hard to comprehend and explain for people without sufficient knowledge and expertise. A lack of understanding may result in problems with the interpretation and application of insights derived from the data.
Python Text Analysis
Python is widely used for text analysis thanks to its wide variety of available resources and tools. It provides flexible and customizable solutions for professionals of different industries and skill levels. Python allows users to perform tasks such as text preprocessing, sentiment analysis, topic modeling, and more. Conducting text analysis in Python offers several advantages such as simplicity, extensive library support, and ease of integration. However, Python requires proficiency in programming and statistical concepts and is not as fast for large-scale processing.
Text Analysis Software
Effective data analysis and actionable insights are key to the success of any business. With the right text analysis API, it doesn’t have to be difficult. If you choose the SaaS approach, no coding or data expertise is needed on your part. All you have to do is upload your data sources.
If you’re looking for a simple cost-effective tool to analyze your text data, Essense might be a perfect match for you. As an AI-powered feedback analysis tool, it performs topic modeling and sentiment analysis, analyzes several data sources at once, and allows you to interact with your data to get an in-depth understanding of your customers' needs. Link your data sources, set up recurring reports, and let Essense take care of the rest.