Sentiment Analysis with Natural Language Processing Next Generation A.I. in InsureTech
In the last article on understanding the role of sentiment analysis in data analytics, we discussed how Natural Language Processing (NLP)-based Sentiment Analysis is an excellent opinion-mining tool to derive deeper insights from the digital space, especially in analyzing insights from structured data. However, it needs to give deeper context and meaning to the same data, which semantic analysis does.
Semantic Analysis is a sub-field of NLP that not just picks up relevant critical phrases in data but finds more profound meaning in natural language while correlating them. The role of machines in understanding and interpreting data is limited, given the vast complexity and subjectivity involved in natural language. But semantics captures this meaning, bringing context, logical sentence structure, and grammar connotation into it.
Classification of Semantic Analytics
Divided into two broad parts, semantic analytics can be classified into;
- Lexical Semantic Analysis – This tries to understand the meaning of each word of the text separately. The definition refers to the dictionary meaning of the word we all know.
- Compositional Semantic Analysis – Even though it is possible to know the meaning of every individual word of a text, it isn’t always possible to understand the whole purpose of the sentence from them.
For example, NLP is an insight-driven branch of AI & AI that uses NLP to derive insights – these are two sentences with the exact ‘root’ words but convey very different meanings.
Compositional semantic analysis tries to understand how different word combinations can derive meaning from texts.
How does Semantic Analysis work?
By understanding the meaning of words by themselves and in combination with others, semantic analysis works according to the below significant processes.
- Word Sense Disambiguation
- Relationship Extraction
Word Sense Disambiguation
In the case of natural language, every word used in a sentence differs in meaning according to context, reference, or occurrence. In Word Sense Disambiguation, the meaning of words is interpreted based on the context of their event in a sentence.
For example, the word ‘fine’ may be ‘making all right’ or ‘payment for defaulting’ based on the context of its usage within the text.
In essence, the machine is doing some part of what we do in verbalizing natural language. For example, this process overcomes the ambiguity in understanding and identifying the meaning of a word based on its context and use.
The semantic analysis does a crucial task called Relationship Extraction, involving identifying different entities present in a sentence and then extracting their relationships to each other.
For example, in the sentence, ‘Charlee.ai led insurance analytics predicts user behavior,’ the entities in red, when correlated, derive the below interpretation,
The ability of Semantic Analysis to derive contextual inferences and meanings from natural language is based on certain key elements that are elaborated on below;
- Hyponymy: This refers to an instance of generic terminology. For example, cats, dogs, and goats are hyponyms, while ‘animals’ is a hypernym.
- Homonymy: Homonymy refers to two or more terminologies that spell the same but with entirely different meanings. For example, ‘Float’ can be ‘moving on water’ or ‘put forth.’ Float is a homonym.
- Synonymy: These are two or more terminologies that spell differently but have the same meaning. Hit/ Strike and Mourn/Grieve are examples of this.
- Antonymy: Antonymy, like the English verbiage, refers to a pair of terms with contrasting meanings but relevant to the same subject, such as Hot/Cold and Big/Small.
- Polysemy: Terms with the exact spelling but various closely related meanings come under polysemy. This is similar to short-tail and long-tail keywords in SEO. Therefore a word like ‘injury‘ may mean ‘skin injury‘ or ‘unforeseen injury or ‘an injury that is severe‘ – having different meanings but bearing a close association.
- Meronomy: Meronomy refers to the relationship of one term to its larger constituent entity. ‘Wheel‘ is, therefore, a meronym of ‘Automobile, ‘and ‘Hands’ are a meronomy of ‘body.’
Semantic analysis is the way machines understand textual information. While for humans, it is simple to understand tone and context; this NLP-based technique represents texts in specific formats to interpret their meaning. This task is done by assessing new data, comparing it against existing past data, and helping predict claims cycle times, severity, and loss while foreseeing user behavior, enabling the insurance industry to save time and money and improve reserve management and risk selection.