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wea_ondara
2020-11-05 18:57:12 +01:00
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@@ -227,26 +227,29 @@ Quality also depends on the type of platform. \cite{lin2017better} showed that e
% valence-base -> continuous % valence-base -> continuous
%%%%% handcrafted - TODO order by sofistication, sentwordnet last %%%%% handcrafted - TODO order by sofistication, sentwordnet last
%liwc (Linguistic Inquiry and Word Count) \cite{pennebaker2001linguistic,pennebakerdevelopment}, 2001 %liwc (Linguistic Inquiry and Word Count) \cite{pennebaker2001linguistic,pennebakerdevelopment}, 2001 %TODO refs wrong?
% - well verified % - well verified
% - ignores acronyms, initialisms, emoticons, or slang, which are known to be important for sentiment analysis of social text (vader) % - ignores acronyms, initialisms, emoticons, or slang, which are known to be important for sentiment analysis of social text (vader)
% - cannot recognise sentiment intensity (all word have an equal weight) (vader) % - cannot recognise sentiment intensity (all word have an equal weight) (vader)
% - ca 4500 words (uptodate?), ca 400 pos words, ca 500 neg words, lexicon proprietary (vader) % - ca 4500 words (uptodate?), ca 400 pos words, ca 500 neg words, lexicon proprietary (vader)
% - TODO list some application examples % - TODO list some application examples
% ... % ...
%General Inquirer (GI) \cite{stone1966general} 1966 %General Inquirer (GI) \cite{stone1966general} 1966 TODO ref wrong?
% - 11k words, 1900 pos, 2300 neg, all approx (vader) % - 11k words, 1900 pos, 2300 neg, all approx (vader)
% - very old (1966), continuously refined, still in use (vader) % - very old (1966), continuously refined, still in use (vader)
% - misses lexical feature detection (acronyms, ...) and sentiment intensity (vader) % - misses lexical feature detection (acronyms, ...) and sentiment intensity (vader)
%Hu-Liu04 \cite{hu2004mining,liu2005opinion}, 2004 %Hu-Liu04 \cite{hu2004mining,liu2005opinion}, 2004
% - focuses on opinion mining, find features in multiple texts (eg reviews) and rate the opinion about the feature, pos/neg binary classification (hu2004mining)
% - does not text summarize opinions but summarizes ratings (hu2004mining)
% - 6800 words, 2000 pos, 4800 neg, all approx values (vader) % - 6800 words, 2000 pos, 4800 neg, all approx values (vader)
% - better suited for social media text, misses acronyms/initialisms (vader) % - better suited for social media text, misses emoticons and acronyms/initialisms (vader)
% - bootstrapped from wordnet (wellknown english lexical database) (vader) % - bootstrapped from wordnet (wellknown english lexical database) (vader, hu2004mining)
%Word-Sense Disambiguation (WSD) \cite{akkaya2009subjectivity}, 2009 %Word-Sense Disambiguation (WSD) \cite{akkaya2009subjectivity}, 2009
% - TODO % - TODO
% - not a sentiment analysis tool per se but can be combined with sentiement analysis tool to distinuish multiple meaning for a word (vader) % - not a sentiment analysis tool per se but can be combined with sentiement analysis tool to distinuish multiple meaning for a word (vader, akkaya2009subjectivity)
% - a word can have multiple meanings, pos neu neg depending on context (vader) % - a word can have multiple meanings, pos neu neg depending on context (vader,akkaya2009subjectivity)
% - derive meaning from context -> disambiguation (vader) % - derive meaning from context -> disambiguation (vader, akkaya2009subjectivity)
% - distinguish subjective and objective word usage, sentences can only contain negative words used in object ways -> sentence not negative, TODO example sentence (akkaya2009subjectivity)
%ANEW (Affective Norms for English Words) \cite{bradley1999affective} 1999 %ANEW (Affective Norms for English Words) \cite{bradley1999affective} 1999
% - lexicon: 1034 words, ranked by pleasure, arousal, and dominance (vader) % - lexicon: 1034 words, ranked by pleasure, arousal, and dominance (vader)
% - words get value 1-9 (neg-pos, continuous), 5 neutral (TODO maybe list word examples with associated value) (vader) % - words get value 1-9 (neg-pos, continuous), 5 neutral (TODO maybe list word examples with associated value) (vader)
@@ -258,14 +261,15 @@ Quality also depends on the type of platform. \cite{lin2017better} showed that e
% - graphmining and dimensionality reduction (vader) % - graphmining and dimensionality reduction (vader)
% - lexicon: 14250 common-sense concepts, with polarity scores [-1,1] continuous, and many other values (vader) % - lexicon: 14250 common-sense concepts, with polarity scores [-1,1] continuous, and many other values (vader)
% - TODO list some concepts (vader) % - TODO list some concepts (vader)
%wordnet \cite{miller1998wordnet} 1998 %wordnet \cite{miller1998wordnet} 1998, maybe exlcude or just mention briefly in sentiwordnet
% - well-known English lexical database (vader) % - well-known English lexical database (vader)
% - group synonyms (synsets) together (vader) % - group synonyms (synsets) together (vader)
% - TODO % - TODO
%sentiwordnet \cite{baccianella2010sentiwordnet} %sentiwordnet \cite{baccianella2010sentiwordnet}
% - extension of wordnet (vader) % - extension of wordnet (vader, baccianella2010sentiwordnet)
% - 147k synset, with 3 values for pos neu neg, sum of synset (TODO pos neu neg?) = 1, range 0-1 continuous (vader) % - 147k synsets (vader),
% - synset values calc by complex mix of semi supervised algorithms (properagtion methods and classifiers) -> not a gold standard lexicon (vader) % - with 3 values for pos neu neg, sum of synset (pos neu neg) = 1, range 0-1 continuous (vader,baccianella2010sentiwordnet)
% - synset values calc by complex mix of semi supervised algorithms (properagtion methods and classifiers) -> not a gold standard lexicon (vader, baccianella2010sentiwordnet)
% - lexicon very noisy, most synset not pos or neg but mix (vader) % - lexicon very noisy, most synset not pos or neg but mix (vader)
% - misses lexical features (vader) % - misses lexical features (vader)