Pandemics are a severe threat to lives in the universe and our universe encounters several pandemics till now. COVID-19 is one of them, which is a viral infectious disease that increased morbidity and mortality worldwide. This has a negative impact on countries’ economies, as well as social and political concerns throughout the world. The growths of social media have witnessed much pandemic-related news and are shared by many groups of people. This social media news was also helpful to analyze the effects of the pandemic clearly. Twitter is one of the social media networks where people shared COVID-19 related news in a wider range. Meanwhile, several approaches have been proposed to analyze the COVID-19 related sentimental analysis. To enhance the accuracy of sentimental analysis, we have proposed a novel approach known as Sentimental Analysis of Twitter social media Data (SATD). Our proposed method is based on five different machine learning models such as Logistic Regression, Random Forest Classifier, Multinomial NB Classifier, Support Vector Machine, and Decision Tree Classifier. These five classifiers possess various advantages and hence the proposed approach effectively classifies the tweets from the Twint. Experimental analyses are made and these classifier models are used to calculate different values such as precision, recall, f1-score, and support. Moreover, the results are also represented as a confusion matrix, accuracy, precision, and receiver operating characteristic (ROC) graphs. From the experimental and discussion section, it is obtained that the accuracy of our proposed classifier model is high.

In recent years, rumours and fake news are spreading widely and very rapidly all over the world. Such circumstances lead to the propagation and production of an inaccurate news article. Also, misinformation and fake news are increased by the user without proper verification. Hence, it is necessary to restrict the spreading of fake information on mass media and to promote confidence all over the world. For this purpose, this paper recognizes the detection of fake news in an effective manner. The proposed methodology in detecting fake news consists of four different phases namely the data pre-processing phase, feature reduction phase, feature extraction phase as well as the classification phase. During data pre-processing, the input data are pre-processed by employing tokenization, stop-words deletion as well as stemming. In the second phase, the features are reduced by employing PPCA to enhance accuracy. Then the extracted feature is provided to the classification phase where LSTM-LF algorithm is utilized to classify the news as fake or real optimally. Furthermore, this paper utilizes four different datasets namely the Buzzfeed dataset, GossipCop dataset, ISOT dataset as well as Politifact dataset for evaluation. The performance evaluation and the comparative analysis are conducted and the analysis reveals that the proposed approach provides better performances when compared to other fake detection-based approaches.

There are various intense forces causing customers to use evaluated data when using social media platforms and microblogging sites. Today, customers throughout the world share their points of view on all kinds of topics through these sources. The massive volume of data created by these customers makes it impossible to analyze such data manually. Therefore, an efficient and intelligent method for evaluating social media data and their divergence needs to be developed. Today, various types of equipment and techniques are available for automatically estimating the classification of sentiments. Sentiment analysis involves determining people's emotions using facial expressions. Sentiment analysis can be performed for any individual based on specific incidents. The present study describes the analysis of an image dataset using CNNs with PCA intended to detect people's sentiments (specifically, whether a person is happy or sad). This process is optimized using a genetic algorithm to get better results. Further, a comparative analysis has been conducted between the different models generated by changing the mutation factor, performing batch normalization, and applying feature reduction using PCA. These steps are carried out across five experiments using the Kaggle dataset. The maximum accuracy obtained is 96.984%, which is associated with the Happy and Sad sentiments.

Sentiment analysis or opinion mining is exploited in business, customer services, and so forth, where people provide their opinions in the form of reviews. However, the people's opinions are in a perplexing form such as, sarcasm, irony, and implied meaning which can cause an impact on sentiment analysis. The only way to analyze these words is through context. Nevertheless, there still exist some issues, to tackle those issues, a lot of research has been conducted by focusing the feature engineering. However, the optimized output has not been acquired yet. Hence, we propose a novel method known as chaotic coyote optimization algorithm (COA) based time weight-AdaBoost support vector machine (SVM) approach which can be used to attain the precise classifications in context. The proposed time weight-AdaBoost SVM can be used to circumvent the drift concept issues and can be utilized for the perfect learning of data for further classifications. Further, the class imbalance issues can be overcome by adopting a modified CO algorithm, that is, the chaotic COA. Furthermore, the proposed work performance is analyzed with state-of-art works such as DICE, ABCDM, and SVM approaches. The comparative analysis shows that our proposed work classifies the social media content acquired from Twitter more accurately than the other works. Thus our work outperforms all the existing approaches in terms of accuracy, precision, recall, and F1 score.

In recent years, social media platforms have gained immense popularity. As a result, there has been a tremendous increase in content on social media platforms. This content can be related to an individual's sentiments, thoughts, stories, advertisements, and news, among many other content types. With the recent increase in online content, the importance of identifying fake and real news has increased. Although, there is a lot of work present to detect fake news, a study on Fuzzy CRNN was not explored into this direction. In this work, a system is designed to classify fake and real news using fuzzy logic. The initial feature extraction process is done using a convolutional recurrent neural network (CRNN). After the extraction of features, word indexing is done with high dimensionality. Then, based on the indexing measures, the ranking process identifies whether news is fake or real. The fuzzy CRNN model is trained to yield outstanding results with 99.99 ± 0.01% accuracy. This work utilizes three different datasets (LIAR, LIAR-PLUS, and ISOT) to find the most accurate model.