Accurate NLP

Sentiment Analysis with NLP Leads to More Accurate Understanding

Language is hard. A single word can completely change the meaning of a sentence. For example, the sentence, “The iPhone has never been good,” is actually a negative statement in spite of the fact that it uses the word “good.” On the other hand, “The iPhone has never been this good” is a positive statement. On the social web, language is even harder to understand because people fill it with colloquialisms, misspellings, slang, and sarcasm. That’s why traditional text analytics produces sentiment analysis that’s wrong more often than it’s right.

The natural language processing (NLP) engine in the NetBase enterprise social intelligence platform reads and understands millions of social media postings every hour. Our deep approach, which employs text analytics and machine learning in combination with NLP, delivers significant advantages in terms of higher accuracy and reliability and broader applicability to social intelligence.

A grammar exercise at Internet scale

For every sentence in every social media post, our NLP engine identifies and links the subjects, objects, verbs, adjectives, and other linguistic patterns. By analyzing this “connective tissue” within each sentence, our NLP engine can account for the complexities in language that have a huge impact on meaning. We then preserve that meaning in a special index within ConsumerBase.

The highest accuracy

Our NLP engine delivers over 80% accuracy, while solutions that rely on statistical keyword-matching algorithms are less than 50% accurate because they never look beyond the context of a single word.

Broad language and “slanguage” support

NetBase understands 45 languages. Plus it knows about:

  • Urban words or “slanguage,” for example “My new phone is sick!”
  • Alternative spellings, for example “luv,” “kewl,” or “gr8”
  • Abbreviations, for example “IMHO,” “ttyl”
  • Common misspellings, for example “teh/the”

The NetBase NLP engine has been designed for the specific lexicon of social media. We are constantly incorporating new rules into our own social media lexicon based on the work of our team of computational linguistics experts, ongoing testing that we do using “crowd-sourced” human evaluators, and on feedback from customers.

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