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\section{Proposed Methods}
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\begin{itemize}
\item Datasets : \insertcontinuationtext
\begin{enumerate}
\item $\text{{Multi Domain Sentiment}}^\text{{[5]}}$ Dataset contains product reviews taken from Amazon.com.
\begin{itemize}
\item BOOKS : 1000 Positive reviews and 1000 Negative reviews.
\end{itemize}
\item $\text{{Movie Reviews}}^\text{{[6]}}$ : \\
\begin{itemize}
\item All html files we collected from the $\text{IMDb archive}^{[7]}$. \\
\item 770 Positive reviews and 703 Negative reviews. \\
\end{itemize}
\end{enumerate}
\item Preprocessing :
\begin{enumerate}
\item \textcolor{violet}{Upper to lower conversion}: All reviews are converted to lower case.
\item \textcolor{violet}{Normalization} : All word with apostrophies should be replace with its orginal form.
\\ eg :$\textcolor{red!=70}{\text{don't} \rightarrow \text{do not}}$
\item \textcolor{violet}{Non ASCII removal} : All non ASCII characters are removed from the reviews. \\ eg :$\textcolor{red!=70}{\bigstar \spadesuit \clubsuit \blacklozenge}$
\item \textcolor{violet}{Remove new lines} : Blank lines are removed from the reviews.
\item \textcolor{violet}{Stopword removal } : Stopwords in English language are \\ \textcolor{red}{an,are,the,a} etc.To remove all such words we are using Natural Language Toolkit$\text{(NLTK)}^{[8]}$. \\
\item \textcolor{violet}{Stemming} : A processing of interface for removing morphological affixes from words. eg:$\text{\textcolor{red!=70}{beauty,beautiness,beautiful}}\Rightarrow \text{\textcolor{red!=70}{beauti}} $
\end{enumerate}
\item Dataset Partitioning :
\begin{enumerate}
\item {\scriptsize {\textbf{MOVIES}}}:
\begin{figure}
\centering
\includegraphics[height=1.8cm,width=4cm]{example-image-a}
\caption{\scriptsize {Fig 1:Dataset Partitioning of MOVIE reviews}}
\end{figure}
\item \scriptsize {\textbf{BOOKS}}:
\begin{figure}
\centering
\includegraphics[height=1.8cm,width=4cm]{example-image-b}
\caption{\scriptsize {Fig 2: Dataset Partitioning of BOOK reviews}}
\end{figure}
\end{enumerate}
\item Feature Selection:
\begin{enumerate}
\item Mutual Infromation :
Selects features that are not uniformly distributed among the classes.
\begin{equation}
\begin{split}
MI(F,C_{k})=(\frac{N_{F,C_{k}}}{N}).log(\frac{N.N_{F,C_{k}}}{N_{F}.N_{C_{k}}})+\\
(\frac{N_{F,\bar{C_{k}}}}{N}).log(\frac{N.N_{F,\bar{C_{k}}}}{N_{F}.N_{\bar{C_{k}}}})+\\
(\frac{N_{\bar{F},C_{k}}}{N}).log(\frac{N.N_{\bar{F},C_{k}}}{N_{\bar{F}}.N_{C_{k}}})+\\
(\frac{N_{\bar{F},\bar{C_{k}}}}{N}).log(\frac{N.N_{\bar{F},\bar{C_{k}}}}{N_{\bar{F}}.N_{\bar{C_{k}}}})\;
\end{split}
\end{equation}
\textit{F} depicts the presence of feature \textit{F} \\
\textit{$\bar{F}$} is the absence of feature \textit{F} \\
\textit{$C_{k}$} is the Positive class \\
\textit{$\bar{C_{k}}$} represents Negative class \\
\textit{N} depicts Total samples
\end{enumerate}
\end{itemize}
% \hyperlink{Architecutre for Sentimental Classification}{\beamerbutton{Diagram}} % Hyperlink...
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每个框架都显示建议方法 I、建议方法 II 等。我想将其更改为建议方法 [1/2]、建议方法 [2/2] 等。
我怎样才能实现这个输出？
答案1
据我所知，投影仪类不提供幻灯片中断点的总数量allowframebreaks。您可以添加
\setbeamertemplate{frametitle continuation}{[\insertcontinuationcount]}
到序言部分获取阿拉伯文计数器。
以下是来自 beamer 手册的一些建议：
allowframebreaks除长篇参考书目外，不要使用该选项。- 不要使用长篇参考书目。
因此，请尝试使用更多框架手动划分内容。我认为这样你会得到更好的结果。:-)