Semantic analysis is the process of machines understanding relationships between words and concepts in text to derive meaning. It involves analyzing grammatical structure and identifying connections between individual words in context. Semantic analysis tools can automatically extract meaningful information from unstructured data like emails and customer feedback. Machine learning algorithms are trained using samples of text labeled with semantic information like word meanings, relationships between entities, and more to enable accurate text analysis. The results can then be used for tasks like text classification, sentiment analysis, intent analysis, keyword and entity extraction.

1. By: Ibrahim Muneer

2. WHAT IS SEMANTIC
ANALYSIS?
Simply put, semantic analysis is the process of drawing
meaning from text. It allows computers to understand and
interpret sentences, paragraphs, or whole documents, by
analyzing their grammatical structure, and identifying
relationships between individual words in a particular
context.
Semantic analysis-driven tools can help companies
automatically extract meaningful information from
unstructured data, such as emails, support tickets, and
customer feedback. Below, we’ll explain how it works.

3.  Lexical semantics plays an important role in semantic analysis,
allowing machines to understand relationships between lexical items
(words, phrasal verbs, etc.):
 Hyponyms: specific lexical items of a generic lexical item (hypernym)
e.g. orange is a hyponym of fruit (hypernym).
 Meronomy: a logical arrangement of text and words that denotes a
constituent part of or member of something e.g., a segment of an
orange
 Polysemy: a relationship between the meanings of words or phrases,
although slightly different, share a common core meaning e.g. I read a
paper, and I wrote a paper)

4.  Synonyms: words that have the same sense or nearly the same
meaning as another, e.g., happy, content, ecstatic, overjoyed
 Antonyms: words that have close to opposite meanings e.g., happy, sad
 Homonyms: two words that are sound the same and are spelled alike
but have a different meaning e.g., orange (color), orange (fruit)
 The semantic analysis also takes into account signs and symbols
(semiotics) and collocations (words that often go together).
 Automated semantic analysis works with the help of machine learning
algorithms.

5.  By feeding semantically enhanced machine learning algorithms with
samples of text, you can train machines to make accurate
predictions based on past observations. There are various sub-tasks
involved in a semantic-based approach for machine learning,
including word sense disambiguation and relationship extraction:
Word Sense Disambiguation
The automated process of identifying in which sense is a word used
according to its context.
Natural language is ambiguous and polysemic; sometimes, the same
word can have different meanings depending on how it’s used.

6.  The word “orange,” for example, can refer to a color, a fruit, or even a city in
Florida!
The same happens with the word “date,” which can mean either a particular day of the
month, a fruit, or a meeting.
In semantic analysis with machine learning, computers use word sense disambiguation to
determine which meaning is correct in the given context.

7. Relationship Extraction
 This task consists of detecting the semantic relationships present in a text.
Relationships usually involve two or more entities (which can be names of people,
places, company names, etc.). These entities are connected through a semantic
category, such as “works at,” “lives in,” “is the CEO of,” “headquartered at.”
 For example, the phrase “Steve Jobs is one of the founders of Apple, which is
headquartered in California” contains two different relationships:

8. Depending on the type of information you’d like to obtain
from data, you can use one of two semantic analysis
techniques: a text classification model (which assigns
predefined categories to text) or a text extractor (which
pulls out specific information from the text).

9.  Topic classification: sorting text into predefined categories based on
its content. Customer service teams may want to classify support
tickets as they drop into their help desk. Through semantic analysis,
machine learning tools can recognize if a ticket should be classified as
a “Payment issue” or a “Shipping problem.”
 Sentiment analysis: detecting positive, negative, or neutral emotions
in a text to denote urgency. For example, tagging Twitter mentions by
sentiment to get a sense of how customers feel about your brand, and
being able to identify disgruntled customers in real time.
 Intent classification: classifying text based on what customers want
to do next. You can use this to tag sales emails as “Interested” and
“Not Interested” to proactively reach out to those who may want to
try your product.

10.  Keyword extraction: finding relevant words and expressions in a text.
This technique is used alone or alongside one of the above methods to
gain more granular insights. For instance, you could analyze the
keywords in a bunch of tweets that have been categorized as
“negative” and detect which words or topics are mentioned most
often.
 Entity extraction: identifying named entities in text, like names of
people, companies, places, etc. A customer service team might find
this useful to automatically extract names of products, shipping
numbers, emails, and any other relevant data from customer support
tickets.
 Automatically classifying tickets using semantic analysis tools
alleviates agents from repetitive tasks and allows them to focus on
tasks that provide more value while improving the whole customer
experience.

11. Tickets can be instantly routed to the right hands, and urgent issues
can be easily prioritized, shortening response times, and keeping
satisfaction levels high.
 nsights derived from data also help teams detect areas of
improvement and make better decisions. For example, you might
decide to create a strong knowledge base by identifying the most
common customer inquiries.