wip

text/2_relwork.tex

@@ -285,13 +285,6 @@ This tools is, by design, better suited for social media texts, although it also
 
 SenticNet \cite{cambria2010senticnet} is also an opinion mining tool but it focuses on concept-level opinions. SenticNet is based on a paradigm called \emph{Sentic Mining} which uses a combination of concepts from artificial integelligence and the Semantic Web. More specifically, it uses graph mining and dimentionality reduction. SenticNets lexicon consists of about 14250 common-sense concepts which a have rating on many scales of which one is a polarity score with a continuous range from -1 to 1. This continuous range of polarity scores enables SenticNet to be sentiment-intensity aware.
 
-%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, 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
 % - tool introducted to compare and standardize research
@@ -318,6 +311,16 @@ Affective Norms for English Words (ANEW) \cite{bradley1999affective} is sentimen
 SentiWordNet \cite{baccianella2010sentiwordnet} is an extension of WordNet and adds ... %TODO whats the difference
 Its lexicon consists of about 147000 synsets, each having 3 value (positive, neutral, negative) attached to them. The each value has a continuous range from 0 to 1 and the sum of these 3 values is set to be 1. The values of each synset are calculated by a mix of semi supervised algorithms, mostly propergation and classifiers. This distinguishes SentiWordNet from previously explained sentiment tools, where the lexica are exclusively created by humans (except for simple mathemtical operations, for instance, averaging of values). Therefore, SentiWordNets lexicon is not considered to be a human-curated gold standard. Furthermore, the lexicon is very noisy and most of the synsets neigher positive or negative but a mix of both\cite{hutto2014vader}. Moreover, SentiWordNet misses lexical features, for instance, acronyms, initalisms and emoticons.
 
+%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, 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)
+
+Word-Sense Disambiguation (WSD)\cite{akkaya2009subjectivity} is not a sentiment analysis tool per se but it can be used to enhance others. In languages certain words have different meanings depending on the context they are used in. When sentiment tools, which do not use WSD, analyze a piece of text, some words which have different meanings depending on the context may skew the resulting sentiment. Some words can even change from positive to negative or vice versa depending on the context. WSD tries to distinguish between subjective and objective word usage. For example: ... %TODO insert example
+
+
 %%%%% 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)
@@ -327,7 +330,7 @@ Its lexicon consists of about 147000 synsets, each having 3 value (positive, neu
 %generalization problem (vader)
 %updateing (extend/modify) hard (e.g. new domain) (vader)
 
-Because hand crafting sentiment analysis requires a lot of effort, researches turned to approaches which offload the labor intensive part to machine learning (ML). However, this results in a new challenge, namely: gathering a \emph good data set to feed the machine learning algorithms for training. Firstly, \emph good data set needs to represent as many features as possible, or otherwise the algorithm will not recognise it. Secondly, the the data set has to be unbiased and representative for all the data of which the data set is a part of. The data set has to represent each feature in an appropiate amount, or otherwise the algorithms may discrimate a feature in favor of other more represented features. These requirements are hard to fulfill and often they are not\cite{hutto2014vader}. After a data set is aquired, a model has to be learned by the ML algorithm, which is, depending on the complexity of the alogrithm, a very computational-intensive and memory-intensive process. After training is completed, the algorithm can predict sentiment values for new pieces of text, which it has never seen before. However, due to the nature of this appraoch, the results cannot be comprehended by humans easily if at all. ML approaches also suffer from an generalization problem and therefore cannot be transfered to other domains without accepting a bad performance, or updating the training data set to fit the new domain. Updating (extending or modify) the training also require a complete training from scratch.
+Because hand crafting sentiment analysis requires a lot of effort, researches turned to approaches which offload the labor intensive part to machine learning (ML). However, this results in a new challenge, namely: gathering a \emph good data set to feed the machine learning algorithms for training. Firstly, \emph good data set needs to represent as many features as possible, or otherwise the algorithm will not recognise it. Secondly, the the data set has to be unbiased and representative for all the data of which the data set is a part of. The data set has to represent each feature in an appropiate amount, or otherwise the algorithms may discrimate a feature in favor of other more represented features. These requirements are hard to fulfill and often they are not\cite{hutto2014vader}. After a data set is aquired, a model has to be learned by the ML algorithm, which is, depending on the complexity of the alogrithm, a very computational-intensive and memory-intensive process. After training is completed, the algorithm can predict sentiment values for new pieces of text, which it has never seen before. However, due to the nature of this appraoch, the results cannot be comprehended by humans easily if at all. ML approaches also suffer from an generalization problem and therefore cannot be transfered to other domains without accepting a bad performance, or updating the training data set to fit the new domain. Updating (extending or modify) the training also require a complete training from scratch. These drawbacks make ML algorithms useful only in narrow situations where changes are not required and the training data is static and unbiased.
 
 % naive bayes
 % - simple (vader)