Semantic Analysis Guide to Master Natural Language Processing Part 9

Semantic analysis, often referred to as meaning analysis, is a process used in linguistics, computer science, and data analytics to derive and understand the meaning of a given text or set of texts. In computer science, it’s extensively used in compiler design, where it ensures that the code written follows the correct syntax and semantics of the programming language. In the context of natural language processing and big data analytics, it delves into understanding the contextual meaning of individual words used, sentences, and even entire documents. By breaking down the linguistic constructs and relationships, semantic analysis helps machines to grasp the underlying significance, themes, and emotions carried by the text.

For example, the word “Bat” is a homonymy word because bat can be an implement to hit a ball or bat is a nocturnal flying mammal also. Just enter the URL of a competitor and you will have access to all the keywords for which it is ranked, with the aim of better positioning and thus optimizing your SEO. The Chrome extension of TextOptimizer, which generates semantic fields, is also very useful when writing content, which avoids constantly using the website. Note that it is also possible to load unpublished content in order to assess its effectiveness. With this report, the algorithm will be able to judge the performance of the content by giving a score that gives a fairly accurate indication of what to optimize on a website. Traditionally, to increase the traffic of your site thanks to SEO, you used to rely on keywords and on the multiplication of the entry doors to your site.

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The columns and rows we’re discarding from our tables are shown as hashed rectangles in Figure 6. Cdiscount, an online retailer of goods and services, uses semantic analysis to analyze and understand online customer reviews. When a user purchases an item on the ecommerce site, they can potentially give post-purchase feedback for their activity. This allows Cdiscount to focus on improving by studying consumer reviews and detecting their satisfaction or dissatisfaction with the company’s products.

What matters in understanding the math is not the algebraic algorithm by which each number in U, V and 𝚺 is determined, but the mathematical properties of these products and how they relate to each other. Let’s say that there are articles strongly belonging to each category, some that are in two and some that belong to all 3 categories. We could plot a table where each row is a different document (a news article) and each column is a different topic. In the cells we would have a different numbers that indicated how strongly that document belonged to the particular topic (see Figure 3). SEMRush is positioned differently than its competitors in the SEO and semantic analysis market. As you can see, to appear in the first positions of a Google search, it is no longer enough to rely on keywords or entry points, but to make sure that the pages of your website are understandable by Google.

Analyze Sentiment in Real-Time with AI

These chatbots act as semantic analysis tools that are enabled with keyword recognition and conversational capabilities. These tools help resolve customer problems in minimal time, thereby increasing customer satisfaction. Moreover, granular insights derived from the text allow teams to identify the areas with loopholes and work on their improvement on priority. By using semantic analysis tools, concerned business stakeholders can improve decision-making and customer experience. Beyond just understanding words, it deciphers complex customer inquiries, unraveling the intent behind user searches and guiding customer service teams towards more effective responses. Semantic analysis stands as the cornerstone in navigating the complexities of unstructured data, revolutionizing how computer science approaches language comprehension.

There are many semantic analysis tools, but some are easier to use than others. Semantic Feature Analysis (SFA) is a method that focuses on extracting and representing word features, helping determine the relationships between words and the significance of individual factors within a text. It involves feature selection, feature weighting, and feature vectors with similarity measurement. Through these techniques, the personal assistant can interpret and respond to user inputs with higher accuracy, exhibiting the practical impact of semantic analysis in a real-world setting. A ‘search autocomplete‘ functionality is one such type that predicts what a user intends to search based on previously searched queries. It saves a lot of time for the users as they can simply click on one of the search queries provided by the engine and get the desired result.

Semantic analysis and self-service work hand in hand to empower users

In other words, we can say that polysemy has the same spelling but different and related meanings. In this component, we combined the individual words to provide meaning in sentences. Lexical analysis is based on smaller tokens but on the contrary, the semantic analysis focuses on larger chunks. This article is part of an ongoing blog series on Natural Language Processing (NLP). I hope after reading that article you can understand the power of NLP in Artificial Intelligence.

As mentioned earlier, semantic frames offer structured representations of events or situations, capturing the meaning within a text. By identifying semantic frames, SCA further refines the understanding of the relationships between words and context. These models assign each word a numeric vector based on their co-occurrence patterns in a large corpus of text. The words with similar meanings are closer together in the vector space, making it possible to quantify word relationships and categorize them using mathematical operations. This provides a foundational overview of how semantic analysis works, its benefits, and its core components.

Improved Machine Learning Models:

Customers benefit from such a support system as they receive timely and accurate responses on the issues raised by them. Moreover, the system can prioritize or flag urgent requests and route them to the respective customer service teams for immediate action with semantic analysis. All factors considered, Uber uses semantic analysis to analyze and address customer support tickets submitted by riders on the Uber platform. The analysis can segregate tickets based on their content, such as map data-related issues, and deliver them to the respective teams to handle.

The analysis of the data is automated and the customer service teams can therefore concentrate on more complex customer inquiries, which require human intervention and understanding. Further, digitised messages, received by a chatbot, on a social network or via email, can be analyzed in real-time by machines, improving employee productivity. This technology is already in use and is analysing the emotion and meaning of exchanges between humans and machines.

What kind of Experience do you want to share?

Read on to find out more about this semantic analysis and its applications for customer service. It’s easier to see the merits if we specify a number of documents and topics. Suppose we had 100 articles and 10,000 different terms (just think of how many unique words there would be all those articles, from “amendment” to “zealous”!). When we start to break our data down into the 3 components, we can actually choose the number of topics — we could choose to have 10,000 different topics, if we genuinely thought that was reasonable. However, we could probably represent the data with far fewer topics, let’s say the 3 we originally talked about. That means that in our document-topic table, we’d slash about 99,997 columns, and in our term-topic table, we’d do the same.

For example, semantic analysis can generate a repository of the most common customer inquiries and then decide how to address or respond to them. In Natural Language, the meaning of a word may vary as per its usage in sentences and the context of the text. Word Sense Disambiguation involves interpreting the meaning of a word based upon the context of its occurrence in a text. With the help of meaning representation, we can link linguistic elements to non-linguistic elements. As we discussed, the most important task of semantic analysis is to find the proper meaning of the sentence.

Keywords

Semantic analysis, the engine behind these advancements, dives into the meaning embedded in the text, unraveling emotional nuances and intended messages. You understand that a customer is frustrated because a customer service agent is taking too long to respond. As such, Cdiscount was able to implement actions aiming to reinforce the conditions around product returns and deliveries (two criteria mentioned often in customer feedback).

By sticking to just three topics we’ve been denying ourselves the chance to get a more detailed and precise look at our data. If we’re looking at foreign policy, we might see terms like “Middle East”, “EU”, “embassies”. For elections it might be “ballot”, “candidates”, “party”; and for reform we might see “bill”, “amendment” or “corruption”.

• Machine Learning has not only enhanced the accuracy of semantic analysis but has also paved the way for scalable, real-time analysis of vast textual datasets.
• Maps are essential to Uber’s cab services of destination search, routing, and prediction of the estimated arrival time (ETA).
• The semantic analysis technology behind these solutions provides a better understanding of users and user needs.
• MedIntel, a global health tech company, launched a patient feedback system in 2023 that uses a semantic analysis process to improve patient care.

However, it comes with its own set of challenges and limitations that can hinder the accuracy and efficiency of language processing systems. These challenges include ambiguity and polysemy, idiomatic expressions, domain-specific knowledge, cultural and linguistic diversity, and computational complexity. Semantic Analysis is a crucial aspect of natural language processing, allowing computers to understand and process the meaning of human languages.

All in all, semantic analysis enables chatbots to focus on user needs and address their queries in lesser time and lower cost. Semantic analysis methods will provide companies the ability to understand the meaning of the text and achieve comprehension and communication levels that are at par with humans. Semantic analysis techniques and tools allow automated text classification or tickets, freeing the concerned staff from mundane and repetitive tasks.