labMTsimple.storyLab functions¶
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labMTsimple.storyLab.
copy_static_files
()¶ Deprecated method to copy files from this module’s static directory into the directory where shifts are being made.
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labMTsimple.storyLab.
emotion
(tmpStr, someDict, scoreIndex=1, shift=False, happsList=[])¶ Take a string and the happiness dictionary, and rate the string.
If shift=True, will return a vector (also then needs the happsList).
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labMTsimple.storyLab.
emotionFileReader
(stopval=1.0, lang='english', min=1.0, max=9.0, returnVector=False)¶ Load the dictionary of sentiment words.
Stopval is our lens, $Delta _h$, read the labMT dataset into a dict with this lens (must be tab-deliminated).
With returnVector = True, returns tmpDict,tmpList,wordList. Otherwise, just the dictionary.
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labMTsimple.storyLab.
emotionV
(frequencyVec, scoreVec)¶ Given the frequency vector and the score vector, compute the happs.
Doesn’t use numpy, but equivalent to np.dot(freq,happs)/np.sum(freq).
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labMTsimple.storyLab.
link_static_files
()¶ Same as copy_static_files, but makes symbolic links.
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labMTsimple.storyLab.
shift
(refFreq, compFreq, lens, words, sort=True)¶ Compute a shift, and return the results.
If sort=True, will return the three sorted lists, and sumTypes. Else, just the two shift lists, and sumTypes (words don’t need to be sorted).
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labMTsimple.storyLab.
shiftHtml
(scoreList, wordList, refFreq, compFreq, outFile, corpus='LabMT', advanced=False, customTitle=False, title='', ref_name='reference', comp_name='comparison', ref_name_happs='', comp_name_happs='', isare='')¶ Make an interactive shift for exploring and sharing.
The most insane-o piece of code here (lots of file copying, writing vectors into html files, etc).
Accepts a score list, a word list, two frequency files and the name of an HTML file to generate
** will make the HTML file, and a directory called static that hosts a bunch of .js, .css that is useful.
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labMTsimple.storyLab.
shiftHtmlJupyter
(scoreList, wordList, refFreq, compFreq, outFile, corpus='LabMT', advanced=False, customTitle=False, title='', ref_name='reference', comp_name='comparison', ref_name_happs='', comp_name_happs='', isare='', saveFull=True, selfshift=False, bgcolor='white')¶ Shifter that generates HTML in two pieces, designed to work inside of a Jupyter notebook.
Saves the filename as given (with .html extension), and sneaks in a filename-wrapper.html, and the wrapper file has the html headers, everything to be a standalone file. The filenamed html is just the guts of the html file, because the complete markup isn’t need inside the notebook.
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labMTsimple.storyLab.
shiftHtmlPreshifted
(scoreList, wordList, refFreq, compFreq, outFile, corpus='LabMT', advanced=False, customTitle=False, title='', ref_name='reference', comp_name='comparison', ref_name_happs='', comp_name_happs='', isare='')¶ Make an interactive shift for exploring and sharing.
The most insane-o piece of code here (lots of file copying, writing vectors into html files, etc).
Accepts a score list, a word list, two frequency files and the name of an HTML file to generate
** will make the HTML file, and a directory called static that hosts a bunch of .js, .css that is useful.
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labMTsimple.storyLab.
stopper
(tmpVec, score_list, word_list, stopVal=1.0, ignore=[], center=5.0)¶ Take a frequency vector, and 0 out the stop words.
Will always remove the nig* words.
Return the 0’ed vector.
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labMTsimple.storyLab.
stopper_mat
(tmpVec, score_list, word_list, stopVal=1.0, ignore=[], center=5.0)¶ Take a frequency vector, and 0 out the stop words.
A sparse-aware matrix stopper. F-vecs are rows: [i,:]
Will always remove the nig* words.
Return the 0’ed matrix, sparse.
labMTsimple.speedy sentiDict class¶
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class
labMTsimple.speedy.
sentiDict
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶ An abstract class to score them all.
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makeListsFromDict
()¶ Make lists from a dict, used internally.
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makeMarisaTrie
(save_flag=False)¶ Turn a dictionary into a marisa_trie.
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matcherDictBool
(word)¶ MatcherTrieDict(word) just checks if a word is in the dict.
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matcherTrieBool
(word)¶ MatcherTrieBool(word) just checks if a word is in the list. Returns 0 or 1.
Works for both trie types. Only one needed to make the plots. Only use this for coverage, so don’t even worry about using with a dict.
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matcherTrieDict
(word, wordVec, count)¶ Not sure what this one does.
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matcherTrieMarisa
(word, wordVec, count)¶ Not sure what this one does.
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my_marisa
= (<marisa_trie.RecordTrie object>, <marisa_trie.RecordTrie object>)¶ Declare this globally.
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openWithPath
(filename, mode)¶ Helper function for searching for files.
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scoreTrieDict
(wordDict, idx=1, center=0.0, stopVal=0.0)¶ Score a wordDict using the dict backend.
INPUTS:
-wordDict is a favorite hash table of word and count.
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scoreTrieMarisa
(wordDict, idx=1, center=0.0, stopVal=0.0)¶ Score a wordDict using the marisa_trie backend.
INPUTS:
-wordDict is a favorite hash table of word and count.
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stopper
(tmpVec, stopVal=1.0, ignore=[])¶ Take a frequency vector, and 0 out the stop words.
Will always remove the nig* words.
Return the 0’ed vector.
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wordVecifyTrieDict
(wordDict)¶ Make a word vec from word dict using dict backend.
INPUTS:
-wordDict is our favorite hash table of word and count.
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wordVecifyTrieMarisa
(wordDict)¶ Make a word vec from word dict using marisa_trie backend.
INPUTS:
-wordDict is our favorite hash table of word and count.
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The following subclasses of the sentiDict class are available:
-LabMT
-ANEW
-LIWC07
-MPQA
-OL
-WK
-LIWC01
-LIWC15
-PANASX
-Pattern
-SentiWordNet
-AFINN
-GI
-WDAL
-EmoLex
-MaxDiff
-HashtagSent
-Sent140Lex
-SOCAL
-SenticNet
-Emoticons
-SentiStrength
-VADER
-Umigon
-USent
-EmoSenticNet
these don’t get the data attributes so we’ll just leave them out
labMTsimple.speedy sentiDict subclasses¶
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class
labMTsimple.speedy.
LabMT
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶ LabMT class.
Now takes the full name of the language.
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class
labMTsimple.speedy.
ANEW
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶ ANEW class.
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loadDict
(bananas, lang)¶ Load the corpus into a dictionary, straight from the origin corpus file.
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class
labMTsimple.speedy.
LIWC07
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶ This is the default, define it anyway
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class
labMTsimple.speedy.
MPQA
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶ MPQA class.
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loadDict
(bananas, lang)¶ Load the corpus into a dictionary, straight from the origin corpus file.
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class
labMTsimple.speedy.
OL
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶ -
loadDict
(bananas, lang)¶ Load the corpus into a dictionary, straight from the origin corpus file.
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class
labMTsimple.speedy.
WK
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
LIWC01
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
LIWC15
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
PANASX
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
Pattern
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
SentiWordNet
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
AFINN
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
GI
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
WDAL
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
EmoLex
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
MaxDiff
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
HashtagSent
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
Sent140Lex
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
SOCAL
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
SenticNet
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
Emoticons
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
SentiStrength
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
VADER
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
Umigon
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
USent
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
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class
labMTsimple.speedy.
EmoSenticNet
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶
labMTsimple.speedy sentiDict subclasses auto¶
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class
labMTsimple.speedy.
AFINN
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
ANEW
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english') ANEW class.
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loadDict
(bananas, lang) Load the corpus into a dictionary, straight from the origin corpus file.
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class
labMTsimple.speedy.
EmoLex
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
EmoSenticNet
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
Emoticons
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
GI
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
HashtagSent
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
LIWC
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')¶ LIWC class.
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loadDict
(bananas, lang)¶ Load the corpus into a dictionary, straight from the origin corpus file.
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class
labMTsimple.speedy.
LIWC01
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
LIWC07
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english') This is the default, define it anyway
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class
labMTsimple.speedy.
LIWC15
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
LabMT
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english') LabMT class.
Now takes the full name of the language.
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class
labMTsimple.speedy.
MPQA
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english') MPQA class.
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loadDict
(bananas, lang) Load the corpus into a dictionary, straight from the origin corpus file.
-
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class
labMTsimple.speedy.
MaxDiff
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
OL
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english') -
loadDict
(bananas, lang) Load the corpus into a dictionary, straight from the origin corpus file.
-
-
class
labMTsimple.speedy.
PANASX
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
Pattern
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
SOCAL
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
Sent140Lex
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
SentiStrength
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
SentiWordNet
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
SenticNet
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
USent
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
Umigon
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
VADER
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
WDAL
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
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class
labMTsimple.speedy.
WK
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english')
-
class
labMTsimple.speedy.
sentiDict
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english') An abstract class to score them all.
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makeListsFromDict
() Make lists from a dict, used internally.
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makeMarisaTrie
(save_flag=False) Turn a dictionary into a marisa_trie.
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matcherDictBool
(word) MatcherTrieDict(word) just checks if a word is in the dict.
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matcherTrieBool
(word) MatcherTrieBool(word) just checks if a word is in the list. Returns 0 or 1.
Works for both trie types. Only one needed to make the plots. Only use this for coverage, so don’t even worry about using with a dict.
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matcherTrieDict
(word, wordVec, count) Not sure what this one does.
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matcherTrieMarisa
(word, wordVec, count) Not sure what this one does.
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my_marisa
= (<marisa_trie.RecordTrie object>, <marisa_trie.RecordTrie object>) Declare this globally.
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openWithPath
(filename, mode) Helper function for searching for files.
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scoreTrieDict
(wordDict, idx=1, center=0.0, stopVal=0.0) Score a wordDict using the dict backend.
INPUTS:
-wordDict is a favorite hash table of word and count.
-
scoreTrieMarisa
(wordDict, idx=1, center=0.0, stopVal=0.0) Score a wordDict using the marisa_trie backend.
INPUTS:
-wordDict is a favorite hash table of word and count.
-
stopper
(tmpVec, stopVal=1.0, ignore=[]) Take a frequency vector, and 0 out the stop words.
Will always remove the nig* words.
Return the 0’ed vector.
-
wordVecifyTrieDict
(wordDict) Make a word vec from word dict using dict backend.
INPUTS:
-wordDict is our favorite hash table of word and count.
-
wordVecifyTrieMarisa
(wordDict) Make a word vec from word dict using marisa_trie backend.
INPUTS:
-wordDict is our favorite hash table of word and count.
-
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labMTsimple.speedy.
u
(x)¶ Python 2/3 agnostic unicode function
test¶
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class
labMTsimple.speedy.
LabMT
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english') LabMT class.
Now takes the full name of the language.
test auto¶
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class
labMTsimple.speedy.
LabMT
(datastructure='dict', stopVal=0.0, bananas=False, loadFromFile=False, saveFile=False, lang='english') LabMT class.
Now takes the full name of the language.