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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

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tidyr.tidyverse.org

Unnest expands a list-column containing data frames into rows and columns.

dplyr.tidyverse.org

These functions provide a framework for modifying rows in a table using a second table of data. The two tables are matched by a set of key variables whose values typically uniquely identify each row. The functions are inspired by SQL's INSERT, UPDATE, and DELETE, and can optionally modify in_place for selected backends. rows_insert() adds new rows (like INSERT). By default, key values in y must not exist in x. rows_append() works like rows_insert() but ignores keys. rows_update() modifies existing rows (like UPDATE). Key values in y must be unique, and, by default, key values in y must exist in x. rows_patch() works like rows_update() but only overwrites NA values. rows_upsert() inserts or updates depending on whether or not the key value in y already exists in x. Key values in y must be unique. rows_delete() deletes rows (like DELETE). By default, key values in y must exist in x.

statology.org

Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I've worked on machine learning algorithms for

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