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@@ -205,7 +205,7 @@ Quality also depends on the type of platform. \cite{lin2017better} showed that e
% A comprehensive survey and classification of approaches for community question answering \cite{srba2016comprehensive}, meta study on papers published between 2005 and 2014 % A comprehensive survey and classification of approaches for community question answering \cite{srba2016comprehensive}, meta study on papers published between 2005 and 2014
\subsection{Analysis} \section{Analysis}
%general blabla %general blabla
% sentiment intensity (Valence based), lexical features % sentiment intensity (Valence based), lexical features
@@ -215,21 +215,27 @@ Quality also depends on the type of platform. \cite{lin2017better} showed that e
% sentiment analyse: es gibt 10-15 methoden, % sentiment analyse: es gibt 10-15 methoden,
% alle sentiment methoden + vader % alle sentiment methoden + vader
\subsubsection{Sentiment analysis} \subsection{Sentiment analysis}
%challenges (vader) %challenges (vader)
% - coverage (e.g. of lexical features, important in mircoblog texts) % - coverage (e.g. of lexical features, important in mircoblog texts)
% - sentiment intensity (some of the following tools ignore intensity completly (just -1, or 1) % - sentiment intensity (some of the following tools ignore intensity completly (just -1, or 1)
% - creating a human-validated gold standard lexicon is very time consuming/labor intensive, with sentiment valence scores, feature detection and context awareness, % - creating a human-validated gold standard lexicon is very time consuming/labor intensive, with sentiment valence scores, feature detection and context awareness,
%%%%% handcrafted
% polarity-based -> binary
% 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
% - acronyms, initialisms, emoticons, or slang, which are known to be important for sentiment analysis of social text (vader) % - 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) % - 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
% ... % ...
%General Inquirer (GI) \cite{stone1966general} 1966 %General Inquirer (GI) \cite{stone1966general} 1966
% - 11k words, 1900 pos, 2300 neg, all approx % - 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
@@ -238,18 +244,35 @@ Quality also depends on the type of platform. \cite{lin2017better} showed that e
% - bootstrapped from wordnet (wellknown english lexical database) (vader) % - bootstrapped from wordnet (wellknown english lexical database) (vader)
%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)
% - a word can have multiple meanings, pos neu neg depending on context (vader)
% - derive meaning from context -> disambiguation (vader)
%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)
% - therefore captures sentiement intensity (vader)
% - misses lexical features (e.g. acronyms, ...) (vader)
%SenticNet \cite{cambria2010senticnet} 2010
% - concept-level opinion and sentiment analysis tool (vader)
% - sentic mining: combination of AI and Semantic Web (vader)
% - 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
% - well-known English lexical database (vader)
% - group synonyms (synsets) together (vader)
% - TODO % - TODO
%sentiwordnet \cite{baccianella2010sentiwordnet} %sentiwordnet \cite{baccianella2010sentiwordnet}
% - TODO % - extension of wordnet (vader)
%ANEW (Affective Norms for English Words) \cite{bradley1999affective} % - 147k synset, with 3 values for pos neu neg, sum of synset (TODO pos neu neg?) = 1, range 0-1 continuous (vader)
% - TODO % - synset values calc by complex mix of semi supervised algorithms (properagtion methods and classifiers) -> not a gold standard lexicon (vader)
%SenticNet \cite{cambria2010senticnet} % - lexicon very noisy, most synset not pos or neg but mix (vader)
% - TODO % - misses lexical features (vader)
%%%%% automated (machine learning) %%%%% automated (machine learning)
%often require large training sets, compare to creating a lexicon (vader) %often require large training sets, compare to creating a lexicon (vader)
%training data must represent as many features as possible, otherwise feature is not learned, often not the case (vader) %training data must represent as many features as possible, otherwise feature is not learned, often not the case (vader)
%training data should be unbiased, or else wrong learning (NOT VADER)
%very cpu and memory intensive, slow, compare to lexicon-based (vader) %very cpu and memory intensive, slow, compare to lexicon-based (vader)
%derived features not nachvollziehbar as a human (black-box) (vader) %derived features not nachvollziehbar as a human (black-box) (vader)
%generaization problem (vader) %generaization problem (vader)
@@ -283,7 +306,7 @@ Quality also depends on the type of platform. \cite{lin2017better} showed that e
% its % its
% ursprüngliches paper ITS, wie hat man das früher (davor) gemacht % ursprüngliches paper ITS, wie hat man das früher (davor) gemacht
\subsubsection{Trend analysis} \subsection{Trend analysis}