# Micro-Blog Sentiment Classification Method Based on the Personality and Bagging Algorithm
## 4 Micro-Blog Sentiment Analysis Method Based on Personality and Bagging Algorithm
### Ensemble Learning of Basic Emotion Classifier  
A base sentiment classifier for each personality set is constructed using a tagged dataset. Micro-blog texts are sequenced data composed of words, which can have significant long-range dependencies, especially those reflecting sentiment and personality. LSTM is a sequence model, which can be used to construct a sentiment classifier and explore long-range dependencies. In this study, the text is divided into different categories according to personality to obtain multiple training sets. One text may belong to multiple personality sets, and the data can be regarded as a choice with a return. A sentiment classifier is trained for each personality dataset using the LSTM. Each sentiment classifier is used to predict the sentiment tendency of the microblog text, and the results of all six basic classifiers are integrated. The integration process of the sentiment classifier is shown in Figure 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Symbol** & **Symbol Meaning** \\ \hline HC\_Cword & number of highly conscientiousness words in the text \\ HC\_Cemoction & number of highly conscientiousness emoji in the text \\ HA\_Cword & number of highly agreeableness words in the text \\ HA\_Cemoction & number of highly agreeableness emoji in the text \\ LA\_Cword & number of low agreeableness words in the text \\ LA\_Cemoction & number of low agreeableness emoji in the text \\ HE\_Cword & number of highly extraversion words in the text \\ LE\_Cword & number of low extraversion emoji in the text \\ LE\_Cemoction & number of low extraversion emoji in the text \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Text characteristics of a personality prediction. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Rule Name** & **Rule** & **Rule Meaning** \\ \hline High conscientiousness & IF _HC\_Cword_\(\geq p1\)\(\vee\) & When the number of words in the text containing the high \\ judgment rule & HC\_Cemoction \(\geq p2\) & conscientiousness dictionary exceeds p1, or the number of \\  & THEN \(C\) = HC & highly conscientiousness emoji in the text exceeds p2, \\ High agreeableness & IF _LA\_Cword\(\geq p3\)\(\vee\) & When the number of words in the text containing the high \\ judgment rule & LA\_Cemoction \(\geq p4\) & agreeableness dictionary exceeds p3, or the number of \\  & THEN \(A\) = LA & highly agreeableness emoji in the text exceeds p4, the text \\ Low agreeableness & IF _LA\_Cword\(\geq p5\)\(\vee\) & is determined to be highly agreeableness. \\ judgment rule & LA\_Cemoction \(\geq p6\) & When the number of words in the text containing the low \\  & THEN \(A\) = LA & agreeableness emoji in the text exceeds p6, the text is \\ High extraversion & IF _HE\_Cword\(\geq p7\)\(\vee\) & When the number of words in the text containing the high \\  & HE\_Cemoction \(\geq p8\) & extraversion dictionary exceeds p7, or the number of \\ judgment rule & THEN \(E\) = HE & highly extraversion emoji in the text exceeds p8, the text is \\ Low extraversion & IF _LE\_Cword\(\geq p9\)\(\vee\) & determined to be highly extraversion. \\ judgment rule & LE\_Cemoction \(\geq p10\) & When the number of words in the text containing the low \\  & THEN \(E\) = LE & extraversion dictionary exceeds p9, or the number of low \\  & THEN \(E\) = LE & extraversion emoji in the text exceeds p10, the text is \\  & & determined to be low extraversion. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Rule of personality determination. Given a set of microblog texts \((t_{1},t_{2},\cdots,t_{n})\), six LSTM basic sentiment classifiers are used to generate output \(p_{ij}^{+},p_{ij}^{-}\) for each of the microblog texts, where \(p_{ij}^{+}\) and \(p_{ij}^{-}\) represent the positive and negative probabilities of the microblog text computed by the \(j\)-th classifier, respectively. Based on the output of each basic sentiment classifier, the results are integrated to obtain the final emotional polarity.