Twitter sentiment analysis is one of the leading research fields. Most of the researchers were contributed to twitter sentiment analysis in English tweets, but few researchers focus on the multilingual twitter sentiment analysis. Some challenges are hoping for the research solutions in multilingual twitter sentiment analysis. This study presents the implementation of sentiment analysis in multilingual twitter data and improves the data classification up to the adequate level of accuracy. Twitter is the sixth leading social networking site in the world. Active users for twitter in a month are 330 million. People can tweet or re-tweet in their languages and allow users to use emoji’s, abbreviations, contraction words, miss spellings, and shortcut words. The best platform for sentiment analysis is twitter. Multilingual tweets and data sparsity are the two main challenges. In this paper, the MLTSA algorithm gives the solution for these two challenges. MLTSA algorithm divides into two parts. One is detecting and translating non-English tweets into English using natural language processing (NLP). And the second one is an appropriate pre-processing method with NLP support can reduce the data sparsity. The result of the MLTSA with SVM achieves good accuracy by up to 95%.

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Chatbot is a program which provides human conversation using Artificial Intelligence (AI). Chatbots are designed to work as VIRTUAL ASSISTANTS (VA). They themselves provide a platform for the promotions of the Products and Services online. All Higher Educational Institutes provide the complete information through their internet sites for students, which admits the use of social nets such as Facebook, WhatsApp, and College websites. Total-in-All, in any website, searching functionality is required to search for any information and it includes Social Media Applications like Facebook and WhatsApp regular response are utilized. Therefore, Chatbot is an effective auto-response system, and also an instant messaging platform. In this paper, AICMS an AI-Based CollegeBot management system for professional Engineering college system provide the autoresponse to student queries about the college basic information, class timetables, examination schedules related to academics. Many Queries about the subjects and placements can be inputted to the system. Here the system AICMS is designed with Dialogflow which is supported by the Google API. AI and running as a messenger in the Facebook, which takes the input as the text and voice and it provides the response as text and voice. It gives a quick, accurate response to student and staff queries in an interactive fashion.

A network in computers consists of a set of interconnected computers using an appropriate technique. In cloud computing, every client and server is unique and has different processing capability. Each server is independent where resource allocation is an important feature for the system to appear as a single network. So the performance of the system depends on the allocation of work among the servers effectively. It is the combination of various factors like latency, throughput, consistency, reliability, and performance. The concept of dynamic resource balancing can be introduced to efficiently manage the factors to be fulfilled in a distributed network. Every client in the network benefits from dynamic resource balancing. In turn, all tasks benefit from resource balancing. The resource balancing comprises of both physical and logical features. The time, cost, performance must be optimized through resource balancing. The paper describes a model for resource balancing in the system to manage the performance through the Internet in cloud computing. This proposed algorithm can be applied to n-processor dynamic systems. This will prove effective to reduce the server resources