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text/2_relwork.tex

@@ -205,6 +205,86 @@ 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
 
 
+\subsection{Analysis}
+
+%general blabla
+% sentiment intensity (Valence based), lexical features
+
+
+
+
+% sentiment analyse: es gibt 10-15 methoden, 
+% alle sentiment methoden + vader
+\subsubsection{Sentiment analysis}
+
+%challenges (vader)
+% - coverage (e.g. of lexical features, important in mircoblog texts)
+% - 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, 
+
+%%%%% handcrafted
+%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)
+% - 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)
+% ...
+%General Inquirer (GI) \cite{stone1966general} 1966
+% - 11k words, 1900 pos, 2300 neg, all approx
+% - very old (1966), continuously refined, still in use (vader)
+% - misses lexical feature detection (acronyms, ...) and sentiment intensity (vader)
+%Hu-Liu04 \cite{hu2004mining,liu2005opinion}, 2004
+% - 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)
+%Word-Sense Disambiguation (WSD) \cite{akkaya2009subjectivity}, 2009
+% - TODO
+%wordnet \cite{miller1998wordnet} 1998
+% - TODO
+%sentiwordnet \cite{baccianella2010sentiwordnet}
+% - TODO
+%ANEW (Affective Norms for English Words) \cite{bradley1999affective}
+% - TODO
+%SenticNet \cite{cambria2010senticnet}
+% - TODO
+
+%%%%% automated (machine learning)
+%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)
+%very cpu and memory intensive, slow, compare to lexicon-based  (vader)
+%derived features not nachvollziehbar as a human (black-box)  (vader)
+%generaization problem (vader)
+%updateing (extend/modify) hard (e.g. new domain) (vader)
+% naive bayes
+% - simple (vader)
+% - assumption: feature probabilties are indepenend of each other (vader)
+% Maximum Entropy
+% - exponential model + logistic regression (vader)
+% - feature weighting through not assuming indepenence as in naive bayes (vader)
+%svm
+%- mathemtical anspruchsvoll (vader)
+%- seperate datapoints using hyper planes (vader)
+%- long training period (other methods do not need training at all because lexica) (vader)
+
+
+%vader (Valence Aware Dictionary for sEntiment Reasoning)(grob) \cite{hutto2014vader}
+% - 2014
+% - detects acyrnoms, ...
+% - sentiment intensity
+%   - not just 1 and -1 for pos and neg but value in a range
+% - context awareness
+%   - disabliguation of words if they have multiple meanings (contextual meaning)
+
+%general
+%dep on sentiment lexicons, more info in vader 2.1 Sentiment Lexicons
+%vader not binary (pos, neg) but 3 categories
+
+
+
+
+% its 
+% ursprüngliches paper ITS, wie hat man das früher (davor) gemacht
+\subsubsection{Trend analysis}
+
 
 
 

text/bib.bib

@@ -286,3 +286,73 @@
   pages={1341--1352},
   year={2013}
 }
+@article{pennebaker2001linguistic,
+  title={Linguistic inquiry and word count: LIWC 2001},
+  author={Pennebaker, James W and Francis, Martha E and Booth, Roger J},
+  journal={Mahway: Lawrence Erlbaum Associates},
+  volume={71},
+  number={2001},
+  pages={2001},
+  year={2001}
+}
+@article{pennebakerdevelopment,
+  title={The Development and Psychometric Properties of LIWC2007},
+  author={Pennebaker, James W and Chung, Cindy K and Ireland, Molly and Gonzales, Amy and Booth, Roger J}
+}
+@article{stone1966general,
+  title={The general inquirer: A computer approach to content analysis.},
+  author={Stone, Philip J and Dunphy, Dexter C and Smith, Marshall S},
+  year={1966},
+  publisher={MIT press}
+}
+@inproceedings{liu2005opinion,
+  title={Opinion observer: analyzing and comparing opinions on the web},
+  author={Liu, Bing and Hu, Minqing and Cheng, Junsheng},
+  booktitle={Proceedings of the 14th international conference on World Wide Web},
+  pages={342--351},
+  year={2005}
+}
+@inproceedings{hu2004mining,
+  title={Mining and summarizing customer reviews},
+  author={Hu, Minqing and Liu, Bing},
+  booktitle={Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining},
+  pages={168--177},
+  year={2004}
+}
+@inproceedings{akkaya2009subjectivity,
+  title={Subjectivity word sense disambiguation},
+  author={Akkaya, Cem and Wiebe, Janyce and Mihalcea, Rada},
+  booktitle={Proceedings of the 2009 conference on empirical methods in natural language processing},
+  pages={190--199},
+  year={2009}
+}
+@book{miller1998wordnet,
+  title={WordNet: An electronic lexical database},
+  author={Miller, George A},
+  year={1998},
+  publisher={MIT press}
+}
+@inproceedings{baccianella2010sentiwordnet,
+  title={Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining.},
+  author={Baccianella, Stefano and Esuli, Andrea and Sebastiani, Fabrizio},
+  booktitle={Lrec},
+  volume={10},
+  number={2010},
+  pages={2200--2204},
+  year={2010}
+}
+@inproceedings{cambria2010senticnet,
+  title={Senticnet: A publicly available semantic resource for opinion mining.},
+  author={Cambria, Erik and Speer, Robert and Havasi, Catherine and Hussain, Amir},
+  booktitle={AAAI fall symposium: commonsense knowledge},
+  volume={10},
+  number={0},
+  year={2010},
+  organization={Citeseer}
+}
+@techreport{bradley1999affective,
+  title={Affective norms for English words (ANEW): Instruction manual and affective ratings},
+  author={Bradley, Margaret M and Lang, Peter J},
+  year={1999},
+  institution={Technical report C-1, the center for research in psychophysiology~…}
+}