<commit_before>
\chapter{Sentiment Classification}
This section describes the experiments done. High level description and
execution of experiments. Detailed execution and technical details in appendix. 

%#TODO what is sentiment \\
Sentiment is described as "an attitude toward something; regard; opinion."\footnote{Sentiment - Dictionary.com: \url{http://dictionary.reference.com/browse/sentiment?s=t}}
The sentiment is the perceived positivity of the message that the user tries to
communicate. Sentiment is in many cases a personal thing, and can change from
person to person or from setting to setting.  

%#TODO why do we get it \\
Some of the motivation for acquiring the sentiment of a tweet or a sentence, is
that we can say something about a persons state of mind and from that predict
behaviour. We want to use the sentiment to make smart decisions alter. As an
example of usage it would be ideal to find a correlation between sentiment and
stock exchange, thus making us able to increase revenue based with decisions
based on the sentiment. 

%#TODO how do we use it \\
In this thesis we have two main ways of classifying tweets. Word counting and
training a classifier. Both methods require dictionaries of positive and
negative words, \ref{data:dictionaries}. In the classifier we use the dictionary
to extract features from a tweet. And with the word counting we count the
number of positive and negative words. 

\section{Manual Classification}\label{sentiment:manual_classification}
#TODO describe the process of manually classifying tweets. 

\section{Word count classification}
#TODO description of method. 
Simply put we count the positive vs negative words. 

\subsection{Classification}

\paragraph{Polarity} 
\hspace{0pt}\\ 
#TODO calculating the polarity.  
<commit_msg>todo element<commit_after>
\chapter{Sentiment Classification}
This section describes the experiments done. High level description and
execution of experiments. Detailed execution and technical details in appendix. 

%#TODO what is sentiment \\
Sentiment is described as "an attitude toward something; regard; opinion."\footnote{Sentiment - Dictionary.com: \url{http://dictionary.reference.com/browse/sentiment?s=t}}
The sentiment is the perceived positivity of the message that the user tries to
communicate. Sentiment is in many cases a personal thing, and can change from
person to person or from setting to setting.  

%#TODO why do we get it \\
Some of the motivation for acquiring the sentiment of a tweet or a sentence, is
that we can say something about a persons state of mind and from that predict
behaviour. We want to use the sentiment to make smart decisions alter. As an
example of usage it would be ideal to find a correlation between sentiment and
stock exchange, thus making us able to increase revenue based with decisions
based on the sentiment. 

%#TODO how do we use it \\
In this thesis we have two main ways of classifying tweets. Word counting and
training a classifier. Both methods require dictionaries of positive and
negative words, \ref{data:dictionaries}. In the classifier we use the dictionary
to extract features from a tweet. And with the word counting we count the
number of positive and negative words. 

\section{Manual Classification}\label{sentiment:manual_classification}
#TODO describe the process of manually classifying tweets. 

#TODO write about the obama tweet set. That most of the content is rubbish and
very difficult to label positive or negative.

\section{Word count classification}
#TODO description of method. 
Simply put we count the positive vs negative words. 

\subsection{Classification}

\paragraph{Polarity} 
\hspace{0pt}\\ 
#TODO calculating the polarity.  

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