Social Media 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 social media sentiment analysis that’s wrong more often than it is right.
The Natural Language Processing (NLP) engine in the NetBase enterprise social media analytics platform reads and understands millions of social media postings every day. Our deep approach, which employs text analytics and machine learning in combination with NLP, delivers significant advantages in terms of higher accuracy.
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 complexities in language that have a huge impact on meaning.
Broad Language and “Slanguage” Support
NetBase understands 42 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, “the/the”
The 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 linguistic experts, ongoing testing that we do using “crowd-sourced” human evaluators and feedback from customers.