Top 10 pantheon.world Competitors

datausa.io

In 2024, the locations with the highest concentration of Business degree recipients are New York, NY, Phoenix, AZ, and South Hooksett, NH. In 2024, the locations with a relatively high number of Business degree recipients are Rialto, CA, Lauderhill, FL, and Ozark, MO. The most common degree awarded to students studying Business is a bachelors degree.

tokenterminal.com

Explore metrics to better understand the fundamentals of blockchain projects.

glassnode.com

See the crypto market the way the experts do. One clear read, every day, from Risk-Off to Risk-On.

lumivero.com

Join our webinars and events for tips and insights on Lumivero products, including NVivo, Citavi, @Risk, and Decision Tools Suite.

teradata.com

Teradata is the AI platform for the autonomous era, connecting and scaling across any environment.

coefficient.io

We believe spreadsheet automation is the missing link in the modern data stack. Reinventing spreadsheets is ambitious, but the time is now.

r-bloggers.com

sentiment.aiMore powerful sentiment analysis using deep learning models made simple..See github.io page herehttps://benwiseman.github.io/sentiment.ai/OverviewKorn Ferry Institute's AITMI team made sentiment.ai for researchers and tinkerers who want a straight-forward way to use powerful, open source deep learning models to improve their sentiment analyses. Our approach is relatively simple and out performs the current best offerings on CRAN and even Microsoft's Azure Cognitive Services. Given that we felt the current norm for sentiment analysis isn't quite good enough, we decided to open-source our simplified interface to turn Universal Sentence Encoder embedding vectors into sentiment scores.We've wrapped a lot of the underlying hassle up to make the process as simple as possible. In addition to just being cool, this approach solves several problems with traditional sentiment analysis, namely:More robust, can handle spelling mitsakes and mixed case, and can be applied to dieciséis (16) languages!Doesn't need a ridged lexicon, rather it matches to an embedding vector (reduces language to a vector of numbers that capture the information, kind of like a PCA). This means you can get scores for words that are not in the lexicon but are similar to existing words!Choose the context for what negative and positive mean using the sentiment_match() function. For example, you could set positive to mean "high quality" and negative to mean "low quality" when looking at product reviews.Power Because it draws from language embedding models trained on billions of texts, news articles, and wikipedia entries, it is able to detect things such as "I learned so much on my trip to Hiroshima museum last year!" is associated with something positive and that "What happeded to the people of Hiroshima in 1945" is associated with something negative.The power is yours We've designed sentiment.ai such that the community can contribute sentiment models via github. This way, it's easier for the co

trends.pinterest.com

understand the trends your audience is searching. discover trends based on interests, demographics, season and more using pinterest trends.

metaculus.com

Metaculus is an online forecasting platform and aggregation engine working to improve human reasoning and coordination on topics of global importance.