Semantic Analysis: Features, Latent Method & Applications

semantic analysis of text

This semantic analysis, sometimes called word sense disambiguation, is used to determine the meaning of a sentence. Combined with machine learning, sentiment analysis is a powerful tool with multiple applications across different industries. It is already influencing the way brands approach marketing, and the impact will be even more visible as AI becomes smarter and ML algorithms become more advanced. By understanding what makes your customers tick, you can resolve the pain points, anticipate wishes and predict problems. Sophisticated machine learning for sentiment analysis is much more efficient than lexicons. There are many examples where the same word in different contexts shows different emotions.

Python is a popular choice for many applications, including natural language processing. It also has many libraries and tools for text processing and analysis, making it a great choice for NLP. When it comes to building NLP models, there are a few key factors that need to be taken into consideration. A good NLP model requires large amounts of training data to accurately capture the nuances of language. This data is typically collected from a variety of sources, such as news articles, social media posts, and customer surveys. Natural Language Processing technology is being used in a variety of applications, such as virtual assistants, chatbots, and text analysis.

A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling

Syntactic parsing helps the computer to better interpret the meaning of the text. The second step in natural language processing is part-of-speech tagging, which involves tagging each token with its part semantic analysis of text of speech. This step helps the computer to better understand the context and meaning of the text. For example, the token “John” can be tagged as a noun, while the token “went” can be tagged as a verb.

  • As we immerse ourselves in the digital age, the importance of semantic analysis in fields such as natural language processing, information retrieval, and artificial intelligence becomes increasingly apparent.
  • You will find a wide range of digital books which will help you understand better these analysis.
  • In NLP, tokens can be words, phrases, or even individual characters, depending on the specific task at hand.

An effective sentiment analysis software combines various text analysis tools for a more holistic analysis of text data. There should also be a sentiment analysis API that you can integrate into your CRM or other marketing software in your stack. As part of President Barack Obama’s 2012 reelection semantic analysis of text campaign, Obama for America utilized sentiment analysis tools to mine 5.7 million messages from the campaign’s website. The algorithm tagged words from inquiries such as polling or contribution based on pre-given lexicons (a list that assigns a sentiment with any given word).


We conduct a 4 step methodology, making use of regular expression to improve accurate classification of crimes. For example, in England and Wales, police forces report their crime figures on a monthly/ quarterly/ bi-annual/ annual basis. Fulfilling the reporting requirement means an analyst must manually search through 8 different fields looking for the world ‘knife’, working out at roughly 36 days work a year. However, one of the challenges is that there can be a lot of misreported figures in terms of the total number of a particular crime.

semantic analysis of text

What are the semantic parts of a sentence?

  • Names (Proper nouns, pronouns, and determiners)
  • General nouns.
  • Intransitive verbs (‘I-verbs’)
  • Transitive verbs (‘T-verbs’)
  • Adjectives (including numbers and most adverbs)
  • Adpositions (prepositions, postpositions, and conjunctions)
  • Words otherwise difficult to classify.