Gain New Insights Into Data with Semantic Analysis

Computers have a difficult time determining meaning in unstructured text – and there is too much of it for humans to cope with. Consequently, the vast majority of the enterprise’s human intelligence – those man-years of experience that every organization has – is not being used to inform the business decisions that we are making today. Semantic analysis is the solution to this problem.

What Is Semantic Analysis?

Semantic analysis, a crucial component of NLP, focuses on understanding the meaning of words and phrases with more than one meaning. In other words, it allows computers to understand the meaning within text and decipher the human intent behind words, phrases and sentences, enabling your company to gather more insights and make better-informed choices. By deriving new insight from communications with customers—helping train chatbots, for example—Semantic analysis can add a new level of agility to the organization.

At its core, semantic analysis seeks to answer questions like: “What does this text mean?”, “What is the sentiment behind it?” and “How do different concepts relate to one another?” By tackling these questions, semantic analysis provides a deeper understanding of text, allowing for more accurate information retrieval and improved communication between humans and machines.

Unlocking Deeper Meaning with Semantic Analysis Techniques

Semantic analysis employs two different methods to derive information from text or a data corpus, depending upon what you are trying to accomplish. The first method involves text classification, and the second involves text extraction.

Text Classification

With Text Classification, text is labeled based on insights you want to gain from the textual data. For example:

• Sentiment Analysis attempts to match the text with the emotion behind it. This is useful in scenarios where companies want to analyze customer reviews.
• Topic Classification organizes the text into a particular category, like whether a science assignment is about physics, chemistry or math.
• Intent Classification examines the sender’s intent for a message to help the organization better respond to it.

Discover how your business can reveal the meaning of its enterprise data with accurate, consistent and transparent outcomes with automated and assisted text classification processes. To learn more about semantic text classification, check out our website or watch our recent webinar on data harmonization.

Text Extraction

In Text Extraction, the goal is to obtain specific information from the text. The purpose is to make information easier to find within your content, thereby increasing its value. It brings efficiency to the organization in its ability to reuse text for various use cases and audit it.

• Text Re-use: Helps you leverage your content in different contexts quickly and for different use cases.
• Text Auditing: Helps you stay ahead of your competition and fully access the volume of knowledge in your content.
• Text Creation: Helps you focus resources on creating missing content that fills gaps rather than recreating content that already exists.

To learn more about the process, check out our blog post, “Extracting Hidden Value from Your Content.” Another interesting take on the subject is outlined in our blog, “Facts and What They Mean,” which explores how people draw conclusions from data to inform what actions to take.

Advantages and Benefits of Semantic Analysis

A well-executed semantic analysis methodology will bolster your competitive advantage. from improving the customer experience and company performance to improving search relevance and providing valuable insights for new business initiatives. By making it easier to connect users with the right information, semantic analysis boosts engagement, satisfaction and productivity across different fields.

• Build a customer-centric strategy: Semantic analysis contributes to the deployment of a customer-centric strategy, where each business decision is centered on the customer’s needs, boosting customer loyalty and retention.
• Improve company performance: By automating data analysis, semantic analysis offers considerable time savings for teams across the organization, funneling resources to more business-critical functions to improve the bottom line.
• Enhance your SEO strategy: Semantic analysis can help uncover a user’s intent during the search process, enabling the organization to serve up more relevant content.
• Bolster customer self-service solutions: Semantic analysis allows machines to detect human emotions to extract valuable information from unstructured data so more effective self-service options can be created.
• Harness artificial intelligence: When it comes to machine learning and AI, semantic analysis supports feature extraction, sentiment analysis and understanding relationships in data.

Semantic Analysis and Natural Language Processing

GeeksforGeeks’ blog post, “Understanding Semantic Analysis-NLP,” provides a great explanation of the relationship between semantic analysis and NLP:

“Semantic Analysis is a subfield of Natural Language Processing (NLP) that attempts to understand the meaning of Natural Language. Understanding Natural Language might seem a straightforward process to us as humans. However, due to the vast complexity and subjectivity involved in human language, interpreting it is quite a complicated task for machines. Semantic Analysis of Natural Language captures the meaning of the given text while taking into account context, logical structuring of sentences and grammar roles.”

Examples of Semantic Analysis

As DataScientist notes, search is one of the most cited uses of semantic analysis. It helps search engines understand the meaning of text to better position it in their results. It goes beyond repetition, improving the results through the richness of the semantic field. The result is an intelligent search experience that leverages user intent and context for success. Learn more about how Progress Semaphore can help you create an intelligent search experience.

Because semantic analysis can understand the positive, negative or neutral connotation of text, it helps machines better understand customer feedback from social networks, reviews and forums by detecting elements that denote dissatisfaction, discontent or impatience.

By processing large volumes of textual data, semantic analysis helps marketers develop customer knowledge and better understand market trends, resulting in better overall communications. This translates into an overall improved digital experience, leading your customers to more relevant information faster and helping solve problems before they escalate. Learn more about improving customer experiences with semantic analysis.

When it comes to regulatory compliance, semantic analysis helps apply metadata that organizations can use to implement information governance best practices, help mitigate risk and streamline processes. Semantic knowledge models emerge as powerful tools in the governance space to help secure and identify sensitive information in your business context.

Conclusion

Semantic analysis represents a significant leap forward in our ability to interact with and understand language. It makes it possible for machines to understand the meaning of a word, sentence or expression of a text. By harnessing the power of meaning, businesses can unlock valuable insights, enhance user experience and drive smarter decisions. With the proliferation of technologies and the rise of unstructured data, semantic analysis will only become more critical, shaping the future of how we manage and organize data.

To learn more, we invite you to explore two whitepapers, The Semantics of Semaphore and Why Semantic Technologies Make Sense.