Title: A Computationally Efficient Mean Shift Implementation
person("Jonathan", "Lisic", , "jlisic@gmail.com", c("aut", "cre"))
Description: Performs mean shift classification using linear and
k-d tree based nearest neighbor implementations for the Gaussian
Packaged: 2016-08-31 10:19:20 UTC; jonathanlisic

k-d tree based nearest neighbor implementations for the Gaussian,
Epanechnikov, and biweight product kernels.
Packaged: 2017-09-05 04:02:23 UTC; jonathanlisic

Packaged: 2018-06-25 14:06:58 UTC; jonathanlisic

Packaged: 2018-12-02 15:21:58 UTC; jonathanlisic

Packaged: 2021-09-21 01:39:33 UTC; jonathanlisic

Description: A popular technique in text analysis today is sentiment analysis,
or trying to determine the overall emotional attitude of a piece of text
(positive or negative).  We provide a new, basic implementation of a common
method for computing sentiment, whereby words are scored as positive or
negative according to a "dictionary", and then an average of those scores
for the document is produced.  The package uses the 'Hu' and 'Liu' sentiment

Description: Sentiment analysis is a popular technique in text mining.  Roughly
speaking, the technique is an attempt to determine the overall emotional
attitude of a piece of text (i.e., positive or negative).  We provide a new
implementation of a common method for computing sentiment, whereby words are
scored as positive or negative according to a "dictionary", and then an
sum of those scores for the document is produced.  We use the 'Hu' and 'Liu'
sentiment dictionary for determining sentiment.  The scoring function is
'vectorized' by document, and scores for multiple documents are computed in

Description: Sentiment analysis is a popular technique in text mining that
attempts to determine the emotional state of some text. We provide a new
scored as positive or negative according to a dictionary lookup. Then the
sum of those scores is returned for the document. We use the 'Hu' and 'Liu'
for determining sentiment. The scoring function is 'vectorized' by document,
and scores for multiple documents are computed in parallel via 'OpenMP'.