## 5 Conclusions and Future Work  
Considering a few difficulties in the emotion detection and sentiment-analysis-based ML models, we propose an improved social media sentiment analytics technique using advanced NLP to predict the individual state of mind to resist some profound events. The solution connects several social media platforms used by a single user id and observes their posts, comments, and feedback throughout the day. We considered dividing and seaming for two different but complementary counting of each inter- and intra-linked expression through happiness and a neutral state of mind. Using semantic level interoperability, we removed inter-operability issues among distributed user servers of other but dependent  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Value of** & **Abbreviation Form** & **Calculated Value** \\ \hline Mean absolute error & MAE & 0.0015339035 \\ Mean squared error & MSE & 0.0000081479 \\ Root mean squared error on prediction & RMSE & 0.0028544564 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model’s efficiency calculation factors with values.  
Figure 8: Density plot concerning neutral state of mind.  
Figure 9: Scatter plot of the test dataset after the model’s training with best-parameter tuning and selection.  
social media accounts. In brief, the proposed algorithm can track down more than one semantically linked activity from diverse social media platforms by a distinct person with a high success rate. The proposed model could have future scope in different industrial concerns, like emo-robot [29], patient care, elderly care [30], and drug recommendation system [6], etc., successfully after the following modifications:  
* We should train the model for more categorical combinations of features in the field of industrial IoT [31].
* We must improve the model to incorporate multi-level gateways to collect from more than four accounts and user ids [32].
* Currently, the data collection method is manual; we must automate it [33].
* Industrial IoT users could comprise the model with IoT modules via a similar user ID for better human-like assistance [34].
* Furthermore, blockchain security could be incorporated for secured multi-level data collection and, hence, no data loss [35; 36].  
Conceptualization, A.A. and S.B.K.; methodology, S.B.K., S.A. and J.A.; validation, S.B.K. and F.A., formal analysis, A.A., S.B.K., J.A., S.A. and F.A.; data curation, S.B.K. and A.A.; writing--original draft preparation, A.A. and S.B.K.; writing--review and editing, J.A., S.A. and F.A.; visualization, F.A.; project administration, A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deputy for Research and Innovation--Ministry of Education, Kingdom of Saudi Arabia for this research through a grant (NU/IFC/2/SERC/-/9) under the Institutional Funding Committee at Najran University, Kingdom of Saudi Arabia.  
Not applicable.  
The authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education, Kingdom of Saudi Arabia for this research through a grant (NU/IFC/2/SERC/-/9) under the Institutional Funding Committee at Najran University, Kingdom of Saudi Arabia.  
The authors declare no conflict of interest.