Semantic analysis is defined as a process of understanding natural language (text) by extracting insightful information such as context, emotions, and sentiments from unstructured data. This article explains the fundamentals of semantic analysis, how it works, examples, and the top five semantic analysis applications in 2022.
Table of Contents
- What Is Semantic Analysis?
- How Does Semantic Analysis Work?
- Examples of Semantic Analysis
- Top 5 Applications of Semantic Analysis in 2022
What Is Semantic Analysis?
Semantic analysis refers to a process of understanding natural language (text) by extracting insightful information such as context, emotions, and sentiments from unstructured data. It gives computers and systems the ability to understand, interpret, and derive meanings from sentences, paragraphs, reports, registers, files, or any document of a similar kind.
Semantic analysis analyzes the grammatical format of sentences, including the arrangement of words, phrases, and clauses, to determine relationships between independent terms in a specific context. This is a crucial task of natural language processing (NLP) systems. It is also a key component of several machine learning tools available today, such as search engines, chatbots, and text analysis software.
The semantic analysis process begins by studying and analyzing the dictionary definitions and meanings of individual words also referred to as lexical semantics. Following this, the relationship between words in a sentence is examined to provide clear understanding of the context.
When fueled by natural language processing and machine learning, systems of semantic analysis tend to achieve human-level accuracy. Several companies rely heavily on semantic analysis-driven tools that automatically draw valuable data from unstructured data such as emails, client reports, and customer reviews.
Advantages of semantic analysis
Semantic analysis tech is highly beneficial for the customer service department of any company. Moreover, it is also helpful to customers as the technology enhances the overall customer experience at different levels. Let’s understand these key advantages in greater detail.
1. Gaining customer insights
Semantic analysis helps in processing customer queries and understanding their meaning, thereby allowing an organization to understand the customer’s inclination. Moreover, analyzing customer reviews, feedback, or satisfaction surveys helps understand the overall customer experience by factoring in language tone, emotions, and even sentiments.
2. Boosting company performance
Automated semantic analysis allows customer service teams to focus on complex customer inquiries that require human intervention and understanding. Also, machines can analyze the messages received on social media platforms, chatbots, and emails. This improves the overall productivity of the employees as the tech frees them from mundane tasks and allows them to concentrate on critical inquiries or operations.
3. Fine-tuning SEO strategy
Semantic analysis helps fine-tune the search engine optimization (SEO) strategy by allowing companies to analyze and decode users’ searches. For example, understanding users’ Google searches. The approach helps deliver optimized and suitable content to the users, thereby boosting traffic and improving result relevance.
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How Does Semantic Analysis Work?
The semantic analysis method begins with a language-independent step of analyzing the set of words in the text to understand their meanings. This step is termed ‘lexical semantics‘ and refers to fetching the dictionary definition for the words in the text. Subsequently, words or elements are parsed. Each element is designated a grammatical role, and the whole structure is processed to cut down on any confusion caused by ambiguous words having multiple meanings.
Upon parsing, the analysis then proceeds to the interpretation step, which is critical for artificial intelligence algorithms. For example, the word ‘Blackberry’ could refer to a fruit, a company, or its products, along with several other meanings. Moreover, context is equally important while processing the language, as it takes into account the environment of the sentence and then attributes the correct meaning to it.
For example, ‘Blackberry is known for its sweet taste’ may directly refer to the fruit, but ‘I got a blackberry’ may refer to a fruit or a Blackberry product. As such, context is vital in semantic analysis and requires additional information to assign a correct meaning to the whole sentence or language.
Technically, semantic analysis involves:
- Data processing.
- Defining features, parameters, and characteristics of processed data
- Data representation
- Defining grammar for data analysis
- Assessing semantic layers of processed data
- Performing semantic analysis based on the linguistic formalism
Critical elements of semantic analysis
The critical elements of semantic analysis are fundamental to processing the natural language:
- Hyponyms: This refers to a specific lexical entity having a relationship with a more generic verbal entity called hypernym. For example, red, blue, and green are all hyponyms of color, their hypernym.
- Meronomy: Refers to the arrangement of words and text that denote a minor component of something. For example, mango is a meronomy of a mango tree.
- Polysemy: It refers to a word having more than one meaning. However, it is represented under one entry. For example, the term ‘dish’ is a noun. In the sentence, ‘arrange the dishes on the shelf,’ the word dishes refers to a kind of plate.
- Synonyms: This refers to similar-meaning words. For example, abstract (noun) has a synonyms summary–synopsis.
- Antonyms: This refers to words with opposite meanings. For example, cold has the antonyms warm and hot.
- hom*onyms: This refers to words with the same spelling and pronunciation, but reveal a different meaning altogether. For example, bark (tree) and bark (dog).
Apart from these vital elements, the semantic analysis also uses semiotics and collocations to understand and interpret language. Semiotics refers to what the word means and also the meaning it evokes or communicates. For example, ‘tea’ refers to a hot beverage, while it also evokes refreshment, alertness, and many other associations. On the other hand, collocations are two or more words that often go together. For example, fast food, dark chocolate, etc.
Machine learning algorithm-based automated semantic analysis
One can train machines to make near-accurate predictions by providing text samples as input to semantically-enhanced ML algorithms. Such estimations are based on previous observations or data patterns. Machine learning-based semantic analysis involves sub-tasks such as relationship extraction and word sense disambiguation.
Let’s understand each one in further detail:
1. Word sense disambiguation
In semantic analysis, word sense disambiguation refers to an automated process of determining the sense or meaning of the word in a given context. As natural language consists of words with several meanings (polysemic), the objective here is to recognize the correct meaning based on its use.
For example, ‘Raspberry Pi’ can refer to a fruit, a single-board computer, or even a company (UK-based foundation). Hence, it is critical to identify which meaning suits the word depending on its usage.
2. Relationship extraction
Relationship extraction is a procedure used to determine the semantic relationship between words in a text. In semantic analysis, relationships include various entities, such as an individual’s name, place, company, designation, etc. Moreover, semantic categories such as, ‘is the chairman of,’ ‘main branch located a’’, ‘stays at,’ and others connect the above entities.
Let’s consider a phrase as an example. ‘Elon Musk is one of the co-founders of Tesla, which is based in Austin, Texas.’
This phrase illustrates two different relationships.
Elon Musk is the co-founder of Tesla
[Person] [Company]
Tesla is based in Austin, Texas
[Company] [Place]
Semantic analysis techniques
The semantic analysis uses two distinct techniques to obtain information from text or corpus of data. The first technique refers to text classification, while the second relates to text extractor.
1. Semantic classification
Semantic classification implies text classification wherein predefined categories are assigned to the text for faster task completion. Following are the various types of text classification covered under semantic analysis:
- Topic classification: This classifies text into preset categories on the basis of the content type. For example, customer support teams in a company may intend to classify the tickets raised by customers at the help desk into separate categories so that the concerned teams can address them. In this scenario, ML-based semantic analysis tools may recognize tickets based on their content and classify them under a ‘payment concern’ or ‘delayed delivery’ category.
- Sentiment analysis: Today, sentiment analysis is used by several social media platforms such as Twitter, Facebook, Instagram, and others to detect positive, negative, or neutral emotions hidden in text (posts, stories). These sentiments, in a way, denote urgency and may raise ‘call to action’ alarms for respective platforms. Sentiment analysis helps brands identify dissatisfied customers or users in real-time and gets a hint on what customers feel about the brand as a whole.
- Intent classification: Intent classification refers to the classification of text based on customers’ intentions in the context of what they intend to do next. You can use it to tag customers as ‘interested’ or ‘not Interested’ to effectively reach out to those customers who may intend to buy a product or show an inclination toward buying it.
2. Semantic extraction
Semantic extraction refers to extracting or pulling out specific data from the text. Extraction types include:
- Keyword extraction: This technique helps identify relevant terms and expressions in the text and gives deep insights when combined with the above classification techniques.
For example, one can analyze keywords in multiple tweets that have been labeled as positive or negative and then detect or extract words from those tweets that have been mentioned the maximum number of times. One can later use the extracted terms for automatic tweet classification based on the word type used in the tweets.
- Entity extraction: As discussed in the earlier example, this technique is used to identify and extract entities in text, such as names of individuals, organizations, places, and others. This method is typically helpful for customer support teams who intend to extract relevant information from customer support tickets automatically, including customer name, phone number, query category, shipping details, etc.
Semantic analysis techniques and tools allow automated text classification or tickets, freeing the concerned staff from mundane and repetitive tasks. In the larger context, this enables agents to focus on the prioritization of urgent matters and deal with them on an immediate basis. It also shortens response time considerably, which keeps customers satisfied and happy.
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.
For example, semantic analysis can generate a repository of the most common customer inquiries and then decide how to address or respond to them. It leads to high customer satisfaction levels.
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Examples of Semantic Analysis
Semantic analysis plays a vital role in the automated handling of customer grievances, managing customer support tickets, and dealing with chats and direct messages via chatbots or call bots, among other tasks.
Let’s take a look at some of the known examples of semantic analysis:
Uber’s social listening
Uber uses semantic analysis to analyze users’ satisfaction or dissatisfaction levels via social listening. This implies that whenever Uber releases an update or introduces new features via a new app version, the mobility service provider keeps track of social networks to understand user reviews and feelings on the latest app release.
Thus, as and when a new change is introduced on the Uber app, the semantic analysis algorithms start listening to social network feeds to understand whether users are happy about the update or if it needs further refinement.
2. Google’s semantic algorithm – Hummingbird
Google incorporated ‘semantic analysis’ into its framework by developing its tool to understand and improve user searches. The Hummingbird algorithm was formed in 2013 and helps analyze user intentions as and when they use the google search engine. As a result of Hummingbird, results are shortlisted based on the ‘semantic’ relevance of the keywords. Moreover, it also plays a crucial role in offering SEO benefits to the company.
3. Cdiscount’s semantic analysis of customer reviews
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.
Cdiscount started using semantic analysis in 2019. It has immensely helped the company handle user queries associated with product deliveries and product returns.
4. Uber’s customer support platform to improve maps
Maps are essential to Uber’s cab services of destination search, routing, and prediction of the estimated arrival time (ETA). All these services perform well when the app renders high-quality maps. Along with services, it also improves the overall experience of the riders and drivers.
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 platform allows Uber to streamline and optimize the map data triggering the ticket.
5. IBM’s Watson conversation service
IBM’s Watson provides a conversation service that uses semantic analysis (natural language understanding) and deep learning to derive meaning from unstructured data. It analyzes text to reveal the type of sentiment, emotion, data category, and the relation between words based on the semantic role of the keywords used in the text. According to IBM, semantic analysis has saved 50% of the company’s time on the information gathering process.
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Top 5 Applications of Semantic Analysis in 2022
According to a 2020 survey by Seagate technology, around 68% of the unstructured and text data that flows into the top 1,500 global companies (surveyed) goes unattended and unused. With growing NLP and NLU solutions across industries, deriving insights from such unleveraged data will only add value to the enterprises.
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.
These are the top five applications of semantic analysis in 2022:
1. Conversational chatbots
Chatbots are playing an essential role for most organizations. 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.
Chatbots help customers immensely as they facilitate shipping, answer queries, and also offer personalized guidance and input on how to proceed further. Moreover, some chatbots are equipped with emotional intelligence that recognizes the tone of the language and hidden sentiments, framing emotionally-relevant responses to them.
All in all, semantic analysis enables chatbots to focus on user needs and address their queries in lesser time and lower cost.
2. Automated ticketing support
As discussed earlier, semantic analysis is a vital component of any automated ticketing support. It understands the text within each ticket, filters it based on the context, and directs the tickets to the right person or department (IT help desk, legal or sales department, etc.).
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.
3. Sentiment analysis
Several companies are using the sentiment analysis functionality to understand the voice of their customers, extract sentiments and emotions from text, and, in turn, derive actionable data from them. It helps capture the tone of customers when they post reviews and opinions on social media posts or company websites.
With sentiment analysis, companies can gauge user intent, evaluate their experience, and accordingly plan on how to address their problems and execute advertising or marketing campaigns. In short, sentiment analysis can streamline and boost successful business strategies for enterprises.
4. Search engine results
Search engines use semantic analysis to understand better and analyze user intent as they search for information on the web. Moreover, with the ability to capture the context of user searches, the engine can provide accurate and relevant results.
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.
Also, ‘smart search‘ is another functionality that one can integrate with ecommerce search tools. The tool analyzes every user interaction with the ecommerce site to determine their intentions and thereby offers results inclined to those intentions.
5. Language translation
Today, semantic analysis methods are extensively used by language translators. Earlier, tools such as Google translate were suitable for word-to-word translations. However, with the advancement of natural language processing and deep learning, translator tools can determine a user’s intent and the meaning of input words, sentences, and context. All these parameters play a crucial role in accurate language translation.
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Takeaway
As we enter the era of ‘data explosion,’ it is vital for organizations to optimize this excess yet valuable data and derive valuable insights to drive their business goals. Semantic analysis allows organizations to interpret the meaning of the text and extract critical information from unstructured data. Semantic-enhanced machine learning tools are vital natural language processing components that boost decision-making and improve the overall customer experience.
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