Now days, in many real life applications, the sentiment analysis plays very vital role for automatic prediction of human being activities especially on online social networks (OSNs). Therefore since from last decade, the research on opinion mining and sentiment analysis is growing with increasing volume of online reviews available over the social media networks like Facebook OSNs. Sentiment analysis falls under the data mining domain research problem. Sentiment analysis is kind of text mining process used to determine the subjective attitude like sentiment from the written texts and hence becoming the main research interest in domain of natural language processing and data mining. The main task in sentiment analysis is classifying human sentiment with objective of classifying the sentiment or emotion of end users for their specific text on OSNs. There are number of research methods designed already for sentiment analysis. There are many factors like accuracy, efficiency, speed etc. used to evaluate the effectiveness of sentiment analysis methods. The MapReduce framework under the domain of big-data is used to minimize the speed of execution and efficiency recently with many data mining methods. The sentiment analysis for Facebook OSNs messages is very challenging tasks as compared to other sentiment analysis because of misspellings and slang words presence in twitter dataset. In this paper, different solutions recently presented are discussed in detail. Then proposed the new approach for sentiment analysis based on hybrid features extraction methods and multi-class Support Vector Machine (SVM). These algorithms are designed using the Big-data techniques to optimize the performance of sentiment analysis
With the increasing use of encryption in network traffic, anomaly detection in encrypted traffic has become a challenging problem. This study proposes an approach for anomaly detection in encrypted HTTPS traffic using machine learning and compares the performance of different feature selection techniques. The proposed approach uses a dataset of HTTPS traffic and applies various machine learning models for anomaly detection. The study evaluates the performance of the models using various evaluation metrics, including accuracy, precision, recall, F1-score, and area under the curve (AUC). The results show that the proposed approach with feature selection outperforms other existing techniques for anomaly detection in encrypted network traffic. However, the proposed approach has limitations, such as the need for further optimization and the use of a single dataset for evaluation. The study provides insights into the performance of different feature selection techniques and presents future research directions for improving the proposed approach. Overall, the proposed approach can aid in the development of more effective anomaly detection techniques in encrypted network traffic.
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