Natural language processing is a type of artificial intelligence technology, and it’s a component of text analytics. It focuses on understanding unstructured human language and transforming voice or text communications into normalized, structured data suitable for analysis.
NLP has a wide range of applications, and some familiar examples from our daily lives are online shopping product recommendations, voice-activated virtual assistants, and voice-to-text messaging.
Siri and Alexa recognize and interpret human voice commands. Similarly, voice‑to‑text messaging interprets the human voice and converts it to text. Amazon, on the other hand, uses algorithms to “scrape” a user’s purchase history, along with items the user has liked and rated to make product recommendations. These are all applications of NLP.
Web scraping means extracting web content to track events and data that can have potential impact on business operations.
One example of web scraping is tracking the news for hurricanes, tornadoes and storms to assess the impact of these weather events on business operations. This helps businesses to take proactive measures before the actual event.
Other applications such as social media listening can provide valuable insights on analyzing data to gain business intelligence.
Social media listening means tracking mentions and conversations on social media platforms such as Twitter and Facebook. Software is used to monitor certain key words or phrases in social media posts, and an algorithm is used to analyze the data.
One example of social media listening is when marketing companies track social media conversations regarding their brand. This helps them to understand why, where and how these conversations are happening, and what people think about their brand.
Social media listening and web scraping can provide valuable insights that help businesses to be proactive and adjust their strategy.