Understanding Natural Language Processing and Its Role in Sentiment Analysis

Today computers can understand programming languages, but what about teaching them how to understand human language, the language that you and I speak? Natural Language Processing (NLP) is a branch of computer science concerned with enabling computers for accurate analysis, comprehension, and derivation of meaning from human words in order to perform a variety of activities. While advances in the field of NLP have elevated it to new heights, NLP has been around for a long time. The first NLP application was for machine translation from Russian to English, which was developed in the 1950s.

Today, natural language processing (NLP) has come a long way and is poised to revolutionise the search experience! For starters, popular instances of devices that respond to clearly articulated voice commands are Google Search and Siri.


Natural language processing is divided into two stages: natural language understanding (NLU) and natural language generation (NLG) (NLG). As a result, natural language understanding is the initial stage in the lengthy process of teaching machines to comprehend the intricacies of human speech. Natural language generation (NLG) is the next and final step in the process, and it involves making machines with the ability to generate information in natural language or human language.

NLU is more focused on machine learning and offers a wide range of applications, ranging from text categorization to data preservation in relevant categories. This stage is critical since the system or machine will never be able to analyse the data or produce the output unless and until it understands the data and its purpose.

Role of NLP in Sentiment Analysis Applications

Sentiment analysis is a technique based on natural language processing (NLP) for determining the positivity, negativity, or neutrality of data. It is the technology that powers sentiment analysis applications like 3RDi Search. Sentiment analysis applications process textual data to assist organisations in tracking brand and product sentiment in consumer feedback and better understanding customer demands.

Enterprises use sentiment analysis applications, a capability also offered by advanced text analysis tools, to get the information they need to determine client sentiments for a certain brand or product. This technology is also frequently employed in the study of social media trends. Sentiment analysis is one of the many text analysis features available in today's modern text analysis tools.

3RDi Search is a text analysis tool that serves as a powerful sentiment analysis application, with a full spectrum of text mining features.

Steps of the Sentiment Analysis Process

The following are the 3 steps followed by sentiment analysis applications as they process the data.

  • Splitting of the text into the core elements, which include tokens, sentences, phrases, entities (POS tagging).
  • Identification of each topic along with relevant words that are similar in meaning.
  • Allotting a sentiment score to each topic.

The identification of the basic constituents of a text, such as verbs, nouns, adjectives, and adverbs, is known as Part of Speech (POS) tagging, a key step in the working of sentiment analysis applications. Many languages include word formation rules and patterns that may be translated into a computer programme to create a simple POS-tagger. To acquire exact POS-tagging results, which are critical to recognising different phrase combinations, a reliable sentiment analysis system must be built on accurate language processing software.

So, that was about NLP and its significance in sentiment analysis. Looking for a powerful sentiment analysis application to get deeper insights about your enterprise data? Visit www.3rdisearch.com/ to discover an advanced text analysis tool or drop us an email on info@3rdisearch.com and our team will get in touch with you.