## 3 Dataset Analysis
### Analysis of the Literature
#### 3.4.3 Word Analysis
The key area of interest seems to be health, which is probably a consequence of the famous event, the COVID-19 pandemic, which generated a multitude of debates and fake news intended to induce panic within people in the online environment, a subject that attracted the attention of many scientists. The most frequent keywords plus are listed here, in accordance with the number of occurrences: \"social media\"--142 occurrences, \"twitter\"--125 occurrences, \"classification\" --83 occurrences, \"sentiment analysis\"--82 occurrences, \"model\"--79 occurrences, \"information\"--68 occurrences, \"information\"--68 occurrences, \"health\"--67 occurrences, \"impact\"--50 occurrences, \"media\"--47 occurrences, and \"online\"--36 occurrences. In terms of authors' keywords, the leadership position is held by \"natural language processing\" with a great number of 969 occurrences, followed by \"social media\"--609 occurrences, \"sentiment analysis\"--402 occurrences, \"machine learning\"--371 occurrences, \"deep learning\"--272 occurrences, \"twitter\"--246 occurrences, \"COVID-19\"--174 occurrences, \"text mining\"--93 occurrences, \"artificial intelligence\"--76 occurrences, and \"data mining\"--72 occurrences. Please see Table 9.  
Furthermore, by examining these words, one can easily notice the fact that the premise from which we initially started, as most of the articles extracted during the data collection step address sentiment analysis using messages posted on social media platforms, especially Twitter, in the context of COVID-19 pandemic, is further supported. Under this circumstance, the attention is more focused on the most widespread types of artificial intelligence techniques through which the analyses were carried out. \begin{table}
\begin{tabular}{c c} \hline \hline
**Words** & **Occurrences** \\ \hline social media & 142 \\ twitter & 125 \\ classification & 83 \\ sentiment analysis & 82 \\ model & 79 \\ information & 68 \\ health & 67 \\ impact & 50 \\ media & 47 \\ online & 36 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Top 10 most frequent words in keywords plus. \begin{table}
\begin{tabular}{c c} \hline \hline
**Words** & **Occurrences** \\ \hline natural language processing & 969 \\ social media & 609 \\ sentiment analysis & 402 \\ machine learning & 371 \\ deep learning & 272 \\ twitter & 246 \\ COVID-19 & 174 \\ text mining & 93 \\ artificial intelligence & 76 \\ data mining & 72 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Top 10 most frequent words in authors’ keywords. Figure 18 represents a colorful representation of the top 50 words found in keywords plus and authors' keywords. The greater the number of occurrences of the word, the greater its size in the image. Table 10 brings to the foreground the top 10 most frequent bigrams that are found in both abstracts and titles throughout the data collection set. Being in line with expectations, the first position is held by \"social media\", which counted an impressive number of occurrences, 3036 in abstracts and 525 in titles. In second place, for abstracts, \"natural language\" is situated with 1347 occurrences, while for titles, \"sentiment analysis\" presents 236 occurrences. Regarding the third position, one can notice \"language processing\" with 1259 occurrences in abstracts, and \"natural language\" with 139 occurrences in titles. For more details, kindly refer to Table 10.  
Concerning trigrams, the first position, according to the number of occurrences for both abstracts and titles, is held by \"natural language processing\" (abstracts--1246 occurrences, titles--131 occurrences). The second and the third place on top are occupied as follows: for bigrams--\"natural processing NLP\" with 374 occurrences, and \"social media platforms\" with 344 occurrences, while for trigrams--\"social media data\" with 55 occurrences and \"fake news detection\" with 37 occurrences. Please check Table 11.  
Figure 19 represents the co-occurrence network for the terms in the authors' keywords.