Sentimental Analysis is a process of computing and categorizing the expressed opinions of people about certain event, subject or product as positive, negative or neutral. The major objective of Sentimental Analysis is to help data-driven decisions using insights from replies in social media, surveys and product reviews. Sentiment Analysis can be done with words, sentences, documents, features or aspects, concepts, phrases, links, clauses and implications. Recently, there has been a lot of attention on sentiment analysis especially from researchers in the fields of text mining and natural language processing. But due to extreme absence of annotated datasets which are used to train models in various domains, the accuracy of sentiment analysis has been hindered. Many types of research have been done to confront the challenge and enhance sentiment analysis classification. Sentiment analysis is important as it helps in identifying the emotional and attitude states of people. Positive or negative feelings of people can be expressed in different ways. This research article talks about, in subtle terms, the different ways to deal with sentiment analysis mostly in Machine Learning, Lexicon-based, Hybrid and Ontology-based approaches. This research article gives point by point perspective of the distinctive applications and challenges of Sentiment Analysis.
Data generated by the devices and the users in modern times is high in volume and variable in structure. Collectively termed as Big Data, it is difficult to store and process using traditional processing tools. Traditional systems store data on physical servers or cloud resulting in higher cost and space complexity. In this paper, we provide a survey of various state-of-the-art research works done to handle the inefficient storage problem of Big Data. We have provided comparative literature to compare existing works to handle Big Data. As a solution to the problem encountered, we propose to split the Big Data into small chunks and provide each chunk to a different cluster for removing the redundant data and compressing it. Once every cluster has completed its task, the data chunks are combined back and stored on the cloud as compared to physical servers. This effectively reduces storage space and achieves parallel processing, thereby decreasing the processing time for very large data sets.
Within the modern patterns, cloud computing valuable asset clients conceded to pay and utilize mannequins with the aid of getting to all the correct agencies furnished by means of the providers. It without a doubt offers with the possibility of throughout, the whole lot, and whenever management on hobby for stop clients. Builders with modern-day ideas for beginner net suppliers are not looking for the wonderful capital costs in equipment to carry their administration or the human fee to oversee it. The modern-day internet transforms into the irresistible expert to the internet of troubles (IoT) as certificate rising global. For the duration of this IoT as a help broadened in essence substances. Every and every concern of examination figuring is appreciating an important role and its research is ascending with a massive assortment. Each and everyone is speaking about the cutting-edge propensities of dispensed computing. The primary downside on this spot is that there isn’t always a normalization altogether that now not everyone getting into the indistinguishable level. There sq. Diploma many guides by that it’s planned, the scope of them sq. Degree in keeping with the accompanying: characteristic non-compulsory, protection offerings, algorithmic, Mathematical, and so on The Simulation of the effort is completed at spans the product used for the association of reasoning system. The activity gives with the IoT suppliers supplied by using the IoT specialist co-op to the person is that extraordinary amongst other users with the boundaries and metaphysics method.
Industrial developments in the Internet of Things (IoT) have lined the way for new fields for the use of wireless sensor network (WSN) technologies. Agricultural monitoring is a case in point where IoT can help improve production, quality and output yield. The use of WSN and data mining techniques will significantly improve many of the agricultural activities. One such activity is the management of the amount of water in planted fields. In addition, during recent years, WSN has become a more evolving field in precision farming. The key problem in the development of WSN is the use of energy and the improvement of the life of the nodes. This paper provides a systematic analysis of the clustering protocols based on soft computing approaches that are used in the agricultural domain to increase WSN‟s lifetime. Classification is carried out according to different soft computing techniques: swarm intelligence, genetic algorithm, fuzzy logic, neural networks. The survey will then present a comparative analysis of soft computing techniques with a focus on their goals along with their merits and drawbacks. This survey enables the researchers to choose the suitable soft computing technique used by clustering protocols for WSN-based precision agriculture.
A focused crawler goes through the world wide web and selects out those pages that are apropos to a predefined topic and neglects those pages that are not matter of interest. It collects the domain specific documents and is considered as one of the most important ways to gather information. However, centralized crawlers are not adequate to spider meaningful and relevant portions of the Web. A crawler which is scalable and which is good at load balancing can improve the overall performance. Therefore, with the size of web pages increasing over internet day by day, in order to download the pages efficiently in terms of time and increase the coverage of crawlers distributed web crawling is of prime importance. This paper describes about different semantic and non-semantic web crawler architectures: broadly classifying them into Nonsemantic (Serial, Parallel and Distributed) and Semantic (Distributed and focused). An implementation of all the aforementioned types is done using the various libraries provided by Python 3, and a comparative analysis is done among them. The purpose of this paper is to outline how different processes can be run parallelly and on a distributed system and how all these interact with each other using shared variables and message passing algorithms.
