You look at the world from a unique perspective. The data you rely on is unique to your company. And you have unique questions you’d like to answer. Shouldn’t you start with a company that has an extensive track record of delivering useful results from messy text data?
That’s where Lexalytics comes in. We delivered the first commercial sentiment analysis engine in 2004, and we’ve been quietly using machine learning since 2008. That’s nearly 10 years of delivering, integrating, and extracting answers. Tens of thousands of model runs, using a variety of machine learning tools. But rather than just building a model and being done with it, we’ve delivered answers. We’ve answered small questions, conquered enterprise-scale challenges, and everything in between. We process billions of text documents every day, and have delivered over 1012 processed text documents just since we started using machine learning. The bumps, scrapes, and scratches we’ve accumulated over the years have taught us valuable lessons on minimizing risk and maximizing value. In short: Lexalytics delivers results.
Focus on delivering initial results quickly. Start with a small, low-risk system that answers a few specific questions. Establish the value of your solution, then expand its capabilities and add more solutions. Train each system independently, to maximize each one’s ability. By following this method, you will:
First, we break sentences and phrases down to evaluate semantics, syntax, and context, using state-of-the-art unsupervised machine learning.
Then, we leverage supervised and semi-supervised machine learning, natural language processing techniques, and our own highly-evolved dictionaries to perform sentiment analysis, extract named entities, themes, categories, and intentions, and create summaries.
Finally, we reveal complex relationships within the text, like themes and sentiment of individual entities, the sentiment of categories, and even connections between entities, to offer rich context insights.