Microblogging has taken a considerable upturn in recent years, with the growth of microblogging websites like Twitter people have started to share more of their opinions about various pressing issues on such online social networks. A broader understanding of the domain in question is required to make an informed decision. With this motivation, our study focuses on finding overall sentiments of related topics with reference to a given topic. We propose an architecture that combines sentiment analysis and community detection to get an overall sentiment of related topics. We apply that model on the following topics: shopping, politics, covid19 and electric vehicles to understand emerging trends, issues and its possible marketing, business and political implications.

Sentiment analysis and community detection are two very active fields of research in computer science. They are both intimately linked to the modern phenomenon of social media, and can be very useful for extracting valuable information from a large corpus of social media posts. In this paper, we review the basic concepts of both fields and outline some of the algorithms and approaches that have been successfully applied. Finally, we take a look at the instances where both have been applied together.

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Microblogging has taken a considerable upturn in recent years, with the growth of microblogging websites like Twitter people have started to share more of their opinions about various pressing issues on such online social networks. A broader understanding of the domain in question is required to make an informed decision. With this motivation, our study focuses on finding overall sentiments of related topics with reference to a given topic. We propose an architecture that combines sentiment analysis and community detection to get an overall sentiment of related topics. We apply that model on the following topics: shopping, politics, covid19 and electric vehicles to understand emerging trends, issues and its possible marketing, business and political implications.

Different Search techniques were developed over the period, each of them having certain advantages and disadvantages. Improving the search techniques result in better performance thus better efficiency. Data Structures can be improved by some minor tweaks leading to improving the quality of data structure. In Linked List, searching is slow due to sequential search requirement, which can be improved by properly indexing the list thus improving speed. There are various indexing methods such as uniform indexing, Tree based indexing, dense indexing, clustered indexing, etc. This paper focuses on the indexed based searching using an additional lane linked list and equips a method to incorporate different series such as squared series and cubic series which further leads to speed enhancement as compared with traditional indexing counterparts due to their nature of increasing gaps between sequential indexes, which reduces the dependency on the main linked list and increasing the dependency on the lane linked list thus using the nature of series more efficiently as the list size increases.