Semantic analysis refers to a process of analyzing and understanding the meaning conveyed by language, either written or spoken. This analysis involves the use of natural language processing (NLP), text analysis tools, sentiment analysis software, machine learning algorithms and techniques for natural language understanding.
What is Semantic Analysis?
Semantic Analysis is the process of analyzing a piece of text or speech to understand its meaning beyond just what words are being used. It aims to identify underlying meanings and interpret them in a way that makes sense within context. Through this process, we can delve deep into not only what the piece says but also what it implies.
Key Components of Semantic Analysis
The key components involved in semantic analysis include:
- Language Processing - The ability to understand human languages through computational methods like parsing.
- Text Analysis Tools - These tools help with determining important features such as keywords, topics and entities etc.
- Sentiment Analysis Software - Identifies emotions associated with specific parts-of-speech or phrases used.
- Machine Learning Algorithms - Used when there are vast amounts of data that need to be analyzed
- Natural Language Understanding – Techniques developed to enable machines to better utilize NLP by simulating human reading comprehension abilities.
Types of Semantic Analysis
There are different types depending on how they parse information:
- Content-based: looks at content segments such as metadata description schemes or audio descriptions linked via XML markup tags
- Distributional: relies on distribution patterns over time between linguistic elements which highlights similarities
- Linguistic Semantics: focuses on inherent/total relations in structure related terminologies often present contexts/further associations
- Lexical Semantics: concerns itself more with vocabulary driven definitions fusing idiosyncrasies based off their respective social contexts.
Applications for Semantic Analyis
Semantic Analyis has numerous applications across a wide range of industries:
Healthcare Industry
- Used to look for patient data in EMRs and flag potential red flags.
- To identify if proper care or treatment has been provided based on symptoms
Financial Services
- Identifying fraudulent activity by checking accounts for anomalous behavior.
- Tracking sentiment among investors, researchers or reporters that are commonly annotated with public regulatory filings.
Marketing
- Understanding customer feedback including reviews across platforms like Amazon, Yelp, etc.
- Pinpointing what aspects of the product/service rank highly.
Conclusion
In summary, semantic analysis allows us to take a closer look at the meaning of written/spoken words in order to extract greater insights than simply taking statements as surface value. It’s an important field within NLP that continues change how organizations understand their customers through improved communications efforts while increasing efficiency in many business processes.
References
- Jurafsky D., Martin J.H.: Speechand Language Processing: An Introductionto Natural LanguageProcessing (Second Edition). Prentice Hall Press(2008)
- Manning C.D., Hinrich Schütze.: Foundations of StatisticalNaturalLanguageProcessing (Second Edition). MITPress (1999)
- Text Analytics With Python: A Practitioner’s Guide To Natural Language Processing – Dipanjan Sarkar
- The Handbook Of Computational Linguistics And NaturalLanguageProcessing – Ruslan Mitkov.
- Biber D., Conrad S.: Register Variation in English:Theory and Application.(2009)
