Unable to position table in Latex

Question

I am having an issue with the positioning of the table in LaTex.

This is the code for the table:

\begin{tabular}
{|m{2cm}|m{2cm}|m{1.5cm}|l|}
     \hline Title&Methodology&Dataset&Accuracy\\ \hline A Supervised Machine Learning Approach to Detect Fake Online Reviews & Naive Bayes, Decision Tree, Random Forest, and Support Vector Machine & Amazon reviews &91.45\%\\
     \hline Declarative Programming Approach for Fake Review Detection & Declarative Programming Approach & Hotel reviews & 94.87\% \\
     \hline Detecting Fake Reviews through Sentiment Analysis Using Machine Learning Techniques & Sentiment Analysis with Naive Bayes and SVM & Hotel Reviews & 87\% \\
    \hline Fake Review Detection Based on Multiple Feature Fusion and Rolling Collaborative Training & Naive Bayes, Decision Tree, Random Forest, and Support Vector Machine & Amazon reviews & 97.98\% \\
    \hline Impact of Sentiment Analysis in Fake Online Review Detection & Naive Bayes, Random Forest, and Support Vector Machine & E-commerce website & 96.36\% \\
    \hline The Effect of Fake Reviews on e-Commerce During and After the Covid-19 Pandemic: SKL-Based Fake Reviews Detection. & Machine learning algorithm based on the Scikit-Learn library & E-commerce platform & 95.22\% \\
    \hline Using Boosting Approaches to Detect Spam Reviews & AdaBoost, Gradient Boosting, and XGBoost, using various features such as n-grams, part-of-speech tags & E-commerce websites & 95.4\% \\
    \hline   
\end{tabular}

The layout of the document is the IEEE conference paper. The content is divided into two columns and my table is on the right column in the middle. Since my table has 8 columns, it is moving all together to the left column on the next page. I want to break the table so that it could fit in.

I have tried using float, and h! commands, but in vain.

Answer 1

• You not provide MWE (Minimal Working Example), a small but colete document, which we can test as it is.
• Consequently we haven't any information about your document page layout.
• In table fragment you declare six columns but use only four!
• I suggest to use X column type for the first three columns and S column type for the last one, and reduce column separation too.
• Using the tblr of the tabularray package make table code shorter:

\documentclass{article}
%---------------- show page layout. don't use in a real document!
\usepackage{showframe}
\renewcommand\ShowFrameLinethickness{0.15pt}
\renewcommand*\ShowFrameColor{\color{red}}
%---------------------------------------------------------------%
\usepackage{lipsum}% For dummy text. Don't use in a real document
\usepackage{ragged2e}
\usepackage{tabularray}
\UseTblrLibrary{siunitx}
\usepackage{lipsum}
\begin{document}
    \begin{table}[ht]
\begin{tblr}{hlines, vlines,
             colspec = { *{3}{X[j, cmd=\RaggedRight\hskip0pt]} Q[c, m, si={table-format=3.2{%}}]},
             cell{2-Z}{4} = {appto=%},
             colsep = 3pt,
             row{1} = {guard, c}
             }
Title
    &   Methodology
        &   Dataset
            &   Accuracy            \
A Supervised Machine Learning Approach to Detect Fake Online Reviews 
    &   Naive Bayes, Decision Tree, Random Forest, and Support Vector Machine 
        &   Amazon reviews 
            &   91.45   \
Declarative Programming Approach for Fake Review Detection 
    & Declarative Programming Approach 
        & Hotel reviews 
            &   94.87   \
Detecting Fake Reviews through Sentiment Analysis Using Machine Learning Techniques
    &   Sentiment Analysis with Naive Bayes and SVM 
        &   Hotel Reviews 
            &   87      \
Fake Review Detection Based on Multiple Feature Fusion and Rolling Collaborative Training 
    &   Naive Bayes, Decision Tree, Random Forest, and Support Vector Machine 
        &   Amazon reviews
            &   97.98   \
Impact of Sentiment Analysis in Fake Online Review Detection 
    &   Naive Bayes, Random Forest, and Support Vector Machine 
        &   E-commerce website 
            &   96.36   \
The Effect of Fake Reviews on e-Commerce During and After the Covid-19 Pandemic: SKL-Based Fake Reviews Detection. 
    &   Machine learning algorithm based on the Scikit-Learn library 
        &   E-commerce platform 
            &   95.22   \
Using Boosting Approaches to Detect Spam Reviews
    &   AdaBoost, Gradient Boosting, and XGBoost, using various features such as n-grams, part-of-speech tags 
        &   E-commerce websites 
            &   95.4    \
\end{tblr}
    \end{table}
\end{document}

(red lines indicate page layout)