Streaming Big Data Analysis for Real-Time Sentiment based Targeted Advertising

Users of every system expect it to get better. Providing feedback to the owners or management was difficult but with the advent of technology, it has become handy. Users can now post their comments through online blogs, android apps and websites. Due to the enormous data piling up every second causes a problem in analyzing it. In this paper, sentiment analysis is used for analyzing comments and reviews for hospital management system are demonstrated with real time data. The tools, algorithms and methodology that could fetch accurate results is described. Experimental results indicate 90% of accuracy in proposed system. The review report generated would help the hospital management to identify the positive and negative feedback which further assists them in improving their facilities that could not only create customer satisfaction but also enhanced business processes.

Section: Results and Analysismentioning

confidence: 99%

Heart rate Encapsulation and Response Tool using Sentiment Analysis

Prabadevi

Reddy

Deepa

2019

“…al. [12] In this paper, a cloud based framework for continuous focused on publicizing in view of tweet assessment investigation is outlined and actualized utilizing the enormous information handling motor Apache Spark, using its spilling library. Application is intended to advance strategically pitching and give better client bolster [13].…”

Section: Status Of Smartphonementioning

confidence: 99%

A Comparative Analysis of Factor Effecting the Buying Judgement of Smart Phone

Bhalla¹,

Amandeep²,

Jain³

2018

<p>Smart phone has various utilizations to various clients as per their necessities. With sensational rise in the usage of smart phone the individuals are considering different factors while purchasing a smart phone. This paper has put endeavor to reveal the fundamental factors which effect clients in picking up of the smart phone. A sample of 512 responses was taken through questionnaire. An organized questionnaire was planned with five point Likert scale was utilized to meeting respondent’s .Factor analysis and descriptive statistical tools were applied to extricate the basic variables influence cell phone acquiring choice. The result shows that the most important factors are physical attributes, apps and sounds while the less importance is given to other factors such as convenience, price which can also vary by age, service and gender. The future scope of this paper lies in the fact that whether age, occupation, gender makes any difference in purchasing decision of smart phone.</p>

“…In the Figure 3 algorithm for the proposed method using big data as input is given. For each tuple set of streaming big data input [20], the source is maintained. It is useful to find source of data in case of adversary attack.…”

Section: Algorithmmentioning

confidence: 99%

“…It generates these values from past data (domain of sensitive values). For big data, millions of tuples are arriving in one session and randomly counterfeit values get generated [20]. There is probability of similar values getting generated in a group.…”

Section: Distributed Execution Flowmentioning

confidence: 99%

Framework to Avoid Similarity Attack in Big Streaming Dat

Puri¹,

Haritha²

2018

<span>The existing methods for privacy preservation are available in variety of fields like social media, stock market, sentiment analysis, electronic health applications. The electronic health dynamic stream data is available in large quantity. Such large volume stream data is processed using delay free anonymization framework. Scalable privacy preserving techniques are required to satisfy the needs of processing large dynamic stream data. In this paper privacy preserving technique which can avoid similarity attack in big streaming data is proposed in distributed environment. It can process the data in parallel to reduce the anonymization delay. In this paper the replacement technique is used for avoiding similarity attack. Late validation technique is used to reduce information loss. The application of this method is in medical diagnosis, e-health applications, health data processing at third party.</span>