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Summary: People can quickly acquire the news through a variety of sources, including websites, blogs, and social media, among others. The spread of fake news has become easier as a result of the availability of these platforms. Anybody with access to these networks generates and distributes fake news for professional or personal gain. Numerous studies relying on supervised and unsupervised learning techniques are available to address the issue of recognizing fake news. All of those studies, though, have one flaw: they all deliver mostly inaccurate or unmatched results. Poor accuracy is attributed to a variety of factors, including imbalanced datasets, inefficient parameter tuning, poor feature selection, and so on. To tackle these issues, we proposed a novel approach for fake news detection. Initially, the data were obtained from the ISOT dataset and data cleaning is performed. After that, preprocessing is done which includes three major steps such as stemming, stop word removal, and tokenization are carried out. Next to preprocessing, various features that involve name entity recognition‐based features are selected during feature extraction. From this, the short dimensionality features are selected with the help of the ensemble modified independent component analysis model. Finally, the hybrid convolutional neural network‐based Levy flight‐based honey badger algorithm detects fake news. The experiments are simulated using python software with various performance metrics such as accuracy, specificity, sensitivity, precision, and F‐scores to validate the performance of the proposed method. The proposed model offers a precision, recall, and accuracy value of 95%, 97%, and 98% when evaluated with the ISOT dataset. When compared to the existing state‐of‐art methods, the proposed method yielded superior detection results and higher accuracy rates. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: 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. [ABSTRACT FROM AUTHOR]