\begin{table}[]
\centering
\caption{\label{Analysis - 2} Statistics and examples of the datasets of the analysis - 2}
\begin{tabular}{ |p{3cm}||p{4cm}|p{3.2cm}|p{4.3cm}|}
 \hline
 \multicolumn{4}{|c|}{Statistics of the datasets in analysis - 2} \\
 \hline
 \centering \textbf{Data Type}& \textbf{IMDB Genre Classification} &\textbf{Section of GoEmotion}&\textbf{French - German Emotions}\\
 \hline
 \centering \textbf{Train}   & 33,657    & 3,800&1232 \\
 \centering \textbf{Test}& 14,425  & 1,400   &538\\
 \hline
 \hline
 \multicolumn{4}{|c|}{Snippets of the datasets} \\
 \hline
 \centering \textbf{Data Requirement}&\centering \textbf{Dataset}&\centering\textbf{Text}& \textbf{Classification} \\
 \hline
 {Fine-Tuning}&{IMDB Genre Classification}&{Listening in to a conversation between his doctor and parents ... sweetheart Peggy Blue.}   & drama \\
 \hline
 {Fine-Tuning}&{IMDB Genre Classification}&{A brother and sister with a past incestuous  ...who get too close to him.}   & thriller \\
 \hline
 {Target Dataset} &{Section of GoEmotion}&{I’m really sorry about your situation :( ...}   & sadness \\
 \hline
 {Target Dataset} &{Section of GoEmotion}&{It's wonderful because it's awful. At not with}   & admiration \\
 \hline
  {Cross Lingual} &{French - German Emotions}&{Moi moi gros triste..}   & sadness \\
 \hline
 {Cross Lingual} &{French - German Emotions}&{Gleiche Kleidung und die Tür ist auf der anderen Seite schwarz - sieht so echt aus !!}   & approval \\
 \hline
\end{tabular}
\end{table}
\section{Introduction}
TL is when a model is pre-trained on a rich dataset before fine-tuning or using feature-based transfer for a domain-specific task \cite{you2020co,raffel2020exploring,houlsby2019parameter}. The role of TL in carrying out NLP tasks has increased significantly due to the need for understanding task interests in target domains where there is a lack of large enough datasets. In such cases, the transfer of knowledge from other domains is known to mitigate the problems of overfitting and yield strong model performance while carrying out the predictions\cite{alyafeai2020survey,durrani2021transfer}. 

\section{Related Work}
Several works have shown the effectiveness of TL across various domains starting from image segmentation to several tasks in NLP \cite{kim2020effectiveness, salza2022effectiveness,bengio2012deep}. Felbo et al. represented the understanding of emotions, sentiment and sarcasm across the text from various domains using the knowledge from a huge data source and transferring it to the model when the prediction was performed on the target domains \cite{felbo2017using}. There is an improvement in the prediction against the model performance when trained only using the target dataset.       

\subsubsection{Analysis - 1: text classification}
The datasets to be used: 

\begin{itemize}
\item IMDB Movie Reviews Dataset: A large dataset classifying the movie reviews into two classes: positive and negative \cite{maas-EtAl:2011:ACL-HLT2011}. 
\item Small SMS Spam Collection: A section of a large dataset classifying the messages to be spam or not. 
\item French and Spanish Reviews Dataset: A small dataset of reviews in French and Spanish classified into positive and negative.
\end{itemize}

\subsubsection{Analysis - 2: sentimental analysis}
The datasets to be used: 

\begin{itemize}
\item IMDB Genre Classification Dataset: A large dataset classifying the movie into 27 different genres using the descriptions.
\item Small Section of GoEmotions Dataset: A fine grained annotated dataset with sentences being classified into 28 emotions including neutral. 
\item French - German Emotions Dataset: A small dataset with machine translation sentences classified into 13 different emotions. 
\end{itemize}

\begin{itemize}
\item Paraphrase Adversaries from Word Scrambling (PAWS): A large dataset containing human-labeled sentence similarity \cite{paws2019naacl, pawsx2019emnlp}. 
\item Financial Domain Dataset: A small dataset consisting of related ticker names used in the stock exchange.
\item Section of PAWS-X Dataset: A small dataset containing cross-lingual sentence similarity data.  
\end{itemize}

\subsubsection{Knowledge transferred models' performance in text classification}
\label{NT}
{"value": "33,657", "type": "Data Stat.", "dataset": "IMDB Genre Classification", "attribute name": "number of examples", "sub-set/group name": "Train", "dataset features": {"xx": "yy"}} {"value": "3,800", "type": "Data Stat.", "dataset": "Section of GoEmotion", "attribute name": "number of examples", "sub-set/group name": "Train", "dataset features": {"xx": "yy"}} {"value": "1232", "type": "Data Stat.", "dataset": "French - German Emotions", "attribute name": "number of examples", "sub-set/group name": "Train", "dataset features": {"xx": "yy"}} {"value": "14,425", "type": "Data Stat.", "dataset": "IMDB Genre Classification", "attribute name": "number of examples", "sub-set/group name": "Test", "dataset features": {"xx": "yy"}} {"value": "1,400", "type": "Data Stat.", "dataset": "Section of GoEmotion", "attribute name": "number of examples", "sub-set/group name": "Test", "dataset features": {"xx": "yy"}} {"value": "538", "type": "Data Stat.", "dataset": "French - German Emotions", "attribute name": "number of examples", "sub-set/group name": "Test", "dataset features": {"xx": "yy"}}