Understanding people’s written emotions isn’t easy, especially on a large scale. That’s why there are different types of sentiment analysis approaches that are used to overcome this challenge.
Understand and monitor customer opinion and social trends from tweets with Repustate's social listening platform for twitter data analysis.
Here’s a simple task: given the sentence “Any good restaurants near Ballard or Fremont?” figure out what the person is referring to. Where are Ballard and Fremont? What are Ballard or Fremont? This (relatively) simple task illustrates the challenges of AI and machine learning. How do we train a language model to understand the intent of the query and resolve any entities as accurately as a person would but much faster?
Despite the noise and nonsensical hash tags, Twitter actually does contain some useful information. You just need the tools to find it. When Repustate first opened its doors, we saw a lot of content coming from established media sources. The overwhelming majority of content being pushed through our API by our developer partners came from sites like the New York Times and TechCrunch.
A few months ago we began a task of migrating our Arabic sentiment analysis engine from a Python/Cython implementation to a Go implementation. The reason: speed. Go makes asynchronous programming and concurrency a cinch to use and that’s where we were able to realize some crazy speed boosts. Our English language sentiment analysis engine can analyze about 500 documents / second.
A new online surveillance bill was proposed by the Canadian government on Tuesday – and its critics are saying it could be a violation of Canadians’ online privacy. Proposed as a bill to help with government security and to make it easier to catch online predators such as child pornographers – if passed , authorities and the police could gain access to an Internet subscriber’s information.
Python AST (Abstract Syntax Tree) module is pretty darn useful We recently introduced a new set of API calls for our enterprise customers (all customers will soon have access to these APIs) that allows you to create customized rules for categorizing text. For example, let’s say you want to classify Twitter data into one of two categories: Photography or Photoshop.
Oct 13, 2020
Repustate Team
