diff --git a/text/2_relwork.tex b/text/2_relwork.tex
index 32269e4..36f9974 100644
--- a/text/2_relwork.tex
+++ b/text/2_relwork.tex
@@ -227,26 +227,29 @@ Quality also depends on the type of platform. \cite{lin2017better} showed that e
 % valence-base -> continuous
 
 %%%%% 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
 % - 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)
 % - ca 4500 words (uptodate?), ca 400 pos words, ca 500 neg words, lexicon proprietary (vader)
 % - 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)
 % - very old (1966), continuously refined, still in use (vader)
 % - misses lexical feature detection (acronyms, ...) and sentiment intensity (vader)
 %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)
-% - better suited for social media text, misses acronyms/initialisms (vader)
-% - bootstrapped from wordnet (wellknown english lexical database) (vader)
+% - better suited for social media text, misses emoticons and acronyms/initialisms (vader)
+% - bootstrapped from wordnet (wellknown english lexical database) (vader, hu2004mining)
 %Word-Sense Disambiguation (WSD) \cite{akkaya2009subjectivity}, 2009
 % - TODO
-% - not a sentiment analysis tool per se but can be combined with sentiement analysis tool to distinuish multiple meaning for a word (vader)
-% - a word can have multiple meanings, pos neu neg depending on context (vader)
-% - derive meaning from context -> disambiguation (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,akkaya2009subjectivity)
+% - 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
 % - 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)
@@ -258,14 +261,15 @@ Quality also depends on the type of platform. \cite{lin2017better} showed that e
 % - graphmining and dimensionality reduction (vader)
 % - lexicon: 14250 common-sense concepts, with polarity scores [-1,1] continuous, and many other values (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)
 % - group synonyms (synsets) together (vader)
 % - TODO
 %sentiwordnet \cite{baccianella2010sentiwordnet}
-% - extension of wordnet (vader)
-% - 147k synset, with 3 values for pos neu neg, sum of synset (TODO pos neu neg?) = 1, range 0-1 continuous (vader)
-% - synset values calc by complex mix of semi supervised algorithms (properagtion methods and classifiers) -> not a gold standard lexicon (vader)
+% - extension of wordnet (vader, baccianella2010sentiwordnet)
+% - 147k synsets (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)
 % - misses lexical features (vader)