## 3 Dataset Analysis
### Analysis of the Literature
#### 3.4.3 Word Analysis  
The aim of this section is to provide readers with an in-depth word analysis from multiple perspectives. Therefore, through a comprehensive investigation of keywords plus, authors' keywords, bigrams, trigrams, co-occurrence networks, and thematic maps, a \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Paper (First Author,** & & & & \\  & **Year, Journal,** & **Title** & **Methods Used** & **Data** & **Purpose** \\ \hline \multirow{9}{*}{5} & \multirow{9}{*}{[PERSON], 2013, _IEEE Transactions on Multimedia_, [38] & Sensing Trending Topics in Twitter & Doc-p (Document Pivoting), GFeat-p (Graph Feature Fivoting), FFM (Frequent Pattern Mining), SFPM (Sequential FPM), NBgram &
\begin{tabular}{c} Three datasets \\ collected from \\ Twitter: \\ the first \\ \end{tabular} } \\  & & & & \\  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \hline \multirow{9}{*}{7} & \multirow{9}{*}{[PERSON], 2013, _IEEE Transactions on Multimedia_, [38] & Real-Time Crisis Mapping of Natural Disasters Using Social Media & Location-Match Algorithm, Performance Testing, Geoparsing Accuracy Evaluation, Benchmarking, Statistical Analysis of Location Matches & Two Twitter datasets manually marked with locations, streets, and regions & Utilize tweet datasets to develop a real-time crisis-mapping system capable of extracting geographic data for effective catastrophe handling and supervision. \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \hline \multirow{9}{*}{8} & \multirow{9}{*}{Ai Y, 2020, _Journal of Marketing Research_, [41] & Is a Picture Worth a Thousand Words? An Empirical Study of Image Content and Social Media Engagement & Propensity Score Matching, Bivariate Zero-Inflated Negative Binomial Model, Log-Linear Regression & Two datasets from Twitter and online like like & Example the way in which image content can influence the popularity of a post from Twitter from Twitter and Instagram. \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \hline \multirow{9}{*}{9} & \multirow{9}{*}{[PERSON], 2015, _Journal of Biomedical Informatics_, [42] & Portable automatic text classification for adverse drug reaction detection via multi-corpus training & Natural Language Processing, Machine Learning Classifiers & Twitter, DailyStrength, and the Adverse Drug Events corpus & Improve the detection of messages related to adverse drug reactions from social media platforms, using machine learning techniques and natural language processing. \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \cline{3-6}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._deeper understanding of the topics addressed, areas of interest, subjects encountered, and goals is desired, which will help us draw some conclusions and discover insights into the subject of natural language processing in social media research. The top 10 most frequent words in keywords plus, found in the data collection set, are extracted in Table 8. At first glance, it can be quickly deduced that most of the articles address topics associated with performing sentiment analysis based on the messages posted on online platforms, mainly Twitter.