Studies show that about 59 percent of the world’s population has access to the internet. This figure accounts for the significant amount of text data derived from blogs, reviews, tweets, surveys, and forum discussions. However, a considerable amount of the data extracted from text is unstructured and scattered across the web.
Many data-centric organizations find it challenging to analyze unstructured data in the form of text. Manually processing text data can be time-consuming and daunting. Remember that you can derive actionable insights from correctly gathering, collating, and analyzing text data. That’s where text analysis comes in. Read on to learn more.
Text Analysis Defined
Text mining and text analysis are used interchangeably to describe the process of collecting data through statistical pattern learning. Text analysis combines machine learning, linguistic, and statistical techniques to process significant volumes of unstructured text to derive meaningful insights and patterns. That way, companies can understand text data like emails, tweets, survey responses, product reviews, and support tickets. While text analysis produces qualitative results, text analytics produces quantitative results.
Businesses leverage text mining to improve their products and services by analyzing product reviews and surveys. In contrast, users use text analytics to derive deeper insights from unstructured text. Ultimately, a company can use the insight gained from data analytics with data visualization methods to make better business decisions.
Benefits of Text Analysis
The application of text analytics comes with many benefits. We’ve highlighted some of the benefits of text analysis.
- Brand Monitoring
This is one of the most significant benefits of text analysis. You can follow engagements about your brand in real time, irrespective of where they may appear. Remember that negative reviews can significantly impact your business’s bottom line, considering how fast these reviews spread. With text analysis, you can spot these potential PR crises and handle them accordingly using positive mentions.
- Social Media Monitoring
Text analysis provides users with sufficient customer data to learn people’s perceptions of their brands. Essentially, business leaders can analyze all positive and negative customer feedback from text data generated from social media. You can also monitor the social media mentions of your competitors.
- Enhances Customer Feedback
Growing your customer base is undoubtedly more challenging than retaining existing customers. That’s why businesses must prioritize people’s feedback. It can help you improve customer experience and customer retention. Text analysis enables you to feel the pulse of your customers.
Text Analysis Techniques & Methods
Several techniques exist for analyzing unstructured text. Some standard text analysis techniques include …
Sentiment Analysis
Analysts leverage sentiment analysis to identify the emotions in the unstructured text. The input text typically includes customer interactions, blogs, forum discussions, or social media posts. Some applications of sentiment analysis include measuring customer response to a specific product or service, understanding new trends, and tracking customer behavior.
Named Entity Recognition (NER)
Named entity recognition (NER) can identify named entities like people, organizations, events, and places in unstructured text. Essentially, NER derives nouns from the data and determines their values. Users leverage the NER technique to classify news based on entities featured in them.
Topic Modelling
Analysts use this text analysis technique to find the crucial topics in a substantial volume of text data. Enterprise organizations use a topic model to determine their most successful products.
Term Frequency-Inverse Document Frequency (TF-IDF)
TF-IDF helps to determine the frequency with which a term appears in essential text data. This technique uses an inverse document frequency to filter out non-insightful words, conjunctions, propositions, and articles that occur frequently.
Event Extraction
Event extraction is an upgrade of named entity extraction. This type of text analysis identifies events contained in text content like meetings, mergers, and acquisitions. Analysts must understand the complexities of the semantics of text content to pull it off. Event extraction finds application in areas like link analysis, business risk monitoring, and geospatial analysis. So, moving on to the text analysis methods. The methods involved with gathering and cleaning the unstructured text data include data gathering, data preparation, and text analytics.
