意味スコア分析とは、テキストやデータの意味を定量的に評価する手法です。この分析は、特定のキーワードやフレーズが持つ感情的な価値や重要性を測定するために使用されます。主にマーケティングや顧客フィードバックの分析において、消費者の意見や感情を理解し、戦略を立てるために役立ちます。意味スコア分析を通じて、企業は顧客のニーズや期待に応えるための洞察を得ることができます。

What is Meaning Score?

Meaning score is a numerical value that represents how closely a question and a text are aligned in terms of meaning, rather than mere word matching.

In the mechanism by which generative AI searches and references information, the text on a page is treated as embeddings (a representation of meaning as a collection of numerical values), and it evaluates how semantically aligned it is with the question. Therefore, the proximity of meaning becomes more important than whether keywords are present (Technical Overview of umoren.ai).

• Proximity of Meaning: How closely the theme or meaning of words aligns with the question
• Proximity of Intent: How well it responds to the purpose of the person asking, such as "wanting to know recommendations" or "wanting to compare"

What do you compare in Meaning Score Analysis?

Based on the content that AI actually references and generates, you compare the meaning scores of your own pages against those of competitors.

• Content Referenced by AI: Pages or texts that were actually used in the answers to the targeted questions
• Your Pages vs. Competitor Pages: How semantically close each is to the question and the AI's answers
• Degree of Match: In the analysis screen of umoren.ai, the degree of match for each competitor site is displayed in categories ranging from "High Match (80% or more)" to "Needs Improvement (less than 40%)"

What can you learn from the analysis results?

You can understand the reasons why competitors are being referenced by AI and the aspects or structures that are lacking in your own pages, broken down by headings and paragraphs.

• Strong Parts as Answer Materials: Which headings, paragraphs, or bullet points are likely to be used as materials for AI's answers
• Reasons Competitors are Picked Up: Aspects, structures, and expression differences that your company lacks
• Areas to Fix: Based on the analysis results, you can specify which sections of which pages need to be revised

How do you translate the analysis results into improvements?

You design content that supplements the lacking aspects and structures, and if necessary, implement technical solutions such as structured data.

• When Definitions are Ambiguous: Clearly state the conclusion at the beginning
• When There are No Comparison Criteria: Add comparison tables or judgment axes
• When Pricing or Targets are Missing: Clearly state them as primary information
• When Not Answering Frequently Asked Questions: Add short Q&A and implement structured data for FAQs

At umoren.ai, LLM engineers create content by reverse engineering expressions and structures that accurately convey meaning to AI based on the results of meaning scores. In support, we identify why competitors are being cited through meaning score and competitor analysis (1-2 weeks) and proceed to content design and implementation (Differences Between umoren.ai and Other LLMO Countermeasure Services).

How do you differentiate between QFO Analysis and Meaning Score Analysis?

You extract the perspectives that AI investigates through QFO analysis and then combine them with Meaning Score Analysis to compare which content, your own or your competitors', is closer to those perspectives.

For more on QFO analysis, see What is QFO (Query Fan-Out) Analysis.