Einblick- A Sentimental Analysis And Opinion Mining System For Mobile Networks

2021 ◽  
Vol 10 (3) ◽  
pp. 2165-2174
Keyword(s):  
Language Processing ◽  
Mobile Networks ◽  
Web Mining ◽  
Opinion Mining ◽  
Mobile Network ◽  
Social Medium ◽  
Mining System ◽  
Electronic Networks ◽  
Research Areas ◽  
Data Analyst

Sentimental analysis and popular legal opinion mining are one among the foremost agile research areas in natural language processing and is additionally widely studied in data processing, web mining and text mining. The growing grandness of sentimental analysis coincide with the rise of social medium like reviews, forum discussion, blog, micro blog, Twitter and social electronic networks. A system to implement this technology in mobile network sectors can be very much helpful for any mobile network for quicker interaction with their subscribers as well as enriching their reach of advertisement. Thus ensuring effective and optimized use of their resources to yield desired public response within a proposed time limit.Sentimental analysis helps to decide whether a statement is positive ,neutral or negative. It also helps data analyst to collect public opinion and do research based on that. In sentimental analysis we breakdown text into small parts and then we will identify each statement bearing phrase or components and will assign a score to each component.

Analysis on Opinion Words Extraction in Electronic Product Reviews

2019 ◽  
Vol 10 (1) ◽  
pp. 47-61
Author(s):  
Sint Sint Aung
Keyword(s):  
Language Processing ◽  
Opinion Mining ◽  
Research Area ◽  
Sentiment Classification ◽  
Training Data ◽  
Product Reviews ◽  
Research Areas ◽  
Product Features ◽  
Opinion Extraction ◽  
Training Examples

Online user reviews are increasingly becoming important for measuring the quality of different products and services. Sentiment classification or opinion mining involves studying and building a system that collects data from online and examines the opinions. Sentiment classification is also defined as opinion extraction as the computational research area of subjective information towards different products. Opinion mining or sentiment classification has attracted in many research areas because of its usefulness in natural language processing and other area of applications. Extracting opinion words and product features are also important tasks in opinion mining. In this work an unsupervised approach was proposed to extract opinions and product features without training examples. To obtain the dependency relation between the product aspects and opinions, this work used StanfordCoreNLP dependency parser. From these relations, rules are predified to extract product and opinions. The main advantage of this approach is that there is no need for training data and it has domain independence. Acoording to the experimental results, the modified algorithm gets better results than the double propagation algorithm.

scholarly journals Web Mining System in a Natural Language Processing Based for Social Media Analysis

2019 ◽  
Vol 8 (S3) ◽  
pp. 72-75
Author(s):  
Gadamsetty Vasavi ◽  
T. Sudha
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Web Mining ◽  
Free Form ◽  
Mining System ◽  
Processing Methods ◽  
Social Media Monitoring ◽  
Media Monitoring

Social Media Monitoring and Analysis are the new trends in technology business. The challenge is to extract correct information from free-form texts of social media communication. Natural Language Processing methods are sometimes used in social media monitoring to improve accuracy in extracting information. This paper discusses a web mining system that is based on Natural Language Processing to analyze social media information. In that process, this research examines Natural Language methods that are important for such analysis. Then the traditional web mining steps are discussed along with proposed use of Natural Language Processing methods.

An overview of the 5G mobile network architecture

2018 ◽  
Author(s):  
Phanidra Palagummi ◽  
Vedant Somani ◽  
Krishna M. Sivalingam ◽  
Balaji Venkat
Keyword(s):  
Wireless Network ◽  
Mobile Networks ◽  
Network Architecture ◽  
Mobile Network ◽  
Communication Technologies ◽  
Future Generation ◽  
Network Function Virtualization ◽  
Low Latency ◽  
High Bandwidth ◽  
Network Technologies

Networking connectivity is increasingly based on wireless network technologies, especially in developing nations where the wired network infrastructure is not accessible to a large segment of the population. Wireless data network technologies based on 2G and 3G are quite common globally; 4G-based deployments are on the rise during the past few years. At the same time, the increasing high-bandwidth and low-latency requirements of mobile applications has propelled the Third Generation Partnership Project (3GPP) standards organization to develop standards for the next generation of mobile networks, based on recent advances in wireless communication technologies. This standard is called the Fifth Generation (5G) wireless network standard. This paper presents a high-level overview of the important architectural components, of the advanced communication technologies, of the advanced networking technologies such as Network Function Virtualization and other important aspects that are part of the 5G network standards. The paper also describes some of the common future generation applications that require low-latency and high-bandwidth communications.

Design of GA and Ontology based NLP Frameworks for Online Opinion Mining

2019 ◽  
Vol 13 (2) ◽  
pp. 159-165
Author(s):  
Manik Sharma ◽  
Gurvinder Singh ◽  
Rajinder Singh
Keyword(s):  
Genetic Algorithm ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Opinion Mining ◽  
Hybrid Genetic Algorithm ◽  
Online Reviews ◽  
Middle Tier ◽  
Complete Set ◽  
Mining Model

Background: For almost every domain, a tremendous degree of data is accessible in an online and offline mode. Billions of users are daily posting their views or opinions by using different online applications like WhatsApp, Facebook, Twitter, Blogs, Instagram etc. Objective: These reviews are constructive for the progress of the venture, civilization, state and even nation. However, this momentous amount of information is useful only if it is collectively and effectively mined. Methodology: Opinion mining is used to extract the thoughts, expression, emotions, critics, appraisal from the data posted by different persons. It is one of the prevailing research techniques that coalesce and employ the features from natural language processing. Here, an amalgamated approach has been employed to mine online reviews. Results: To improve the results of genetic algorithm based opining mining patent, here, a hybrid genetic algorithm and ontology based 3-tier natural language processing framework named GAO_NLP_OM has been designed. First tier is used for preprocessing and corrosion of the sentences. Middle tier is composed of genetic algorithm based searching module, ontology for English sentences, base words for the review, complete set of English words with item and their features. Genetic algorithm is used to expedite the polarity mining process. The last tier is liable for semantic, discourse and feature summarization. Furthermore, the use of ontology assists in progressing more accurate opinion mining model. Conclusion: GAO_NLP_OM is supposed to improve the performance of genetic algorithm based opinion mining patent. The amalgamation of genetic algorithm, ontology and natural language processing seems to produce fast and more precise results. The proposed framework is able to mine simple as well as compound sentences. However, affirmative preceded interrogative, hidden feature and mixed language sentences still be a challenge for the proposed framework.

scholarly journals DCSS Protocol for Data Caching and Sharing Security in a 5G Network

Network ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 75-94
Author(s):  
Ed Kamya Kiyemba Edris ◽  
Mahdi Aiash ◽  
Jonathan Loo
Keyword(s):  
Mobile Networks ◽  
Service Providers ◽  
Security Analysis ◽  
Mobile Network ◽  
End Users ◽  
Third Party ◽  
Control Mechanisms ◽  
Security Measures ◽  
Data Caching ◽  
Pi Calculus

Fifth Generation mobile networks (5G) promise to make network services provided by various Service Providers (SP) such as Mobile Network Operators (MNOs) and third-party SPs accessible from anywhere by the end-users through their User Equipment (UE). These services will be pushed closer to the edge for quick, seamless, and secure access. After being granted access to a service, the end-user will be able to cache and share data with other users. However, security measures should be in place for SP not only to secure the provisioning and access of those services but also, should be able to restrict what the end-users can do with the accessed data in or out of coverage. This can be facilitated by federated service authorization and access control mechanisms that restrict the caching and sharing of data accessed by the UE in different security domains. In this paper, we propose a Data Caching and Sharing Security (DCSS) protocol that leverages federated authorization to provide secure caching and sharing of data from multiple SPs in multiple security domains. We formally verify the proposed DCSS protocol using ProVerif and applied pi-calculus. Furthermore, a comprehensive security analysis of the security properties of the proposed DCSS protocol is conducted.

scholarly journals Classification of Fake Product Ratings Using a Timeline Based Approach

2017 ◽  
Vol 3 (2) ◽  
pp. 12 ◽  
Author(s):  
Neha Thomas ◽  
Susan Elias
Keyword(s):  
Language Processing ◽  
Opinion Mining ◽  
Optimal Point ◽  
Linear Classifiers ◽  
Wide Range ◽  
Text Content ◽  
Classification Tool ◽  
Fake Reviews ◽  
Product Ratings

 Abstract— Detection of fake review and reviewers is currently a challenging problem in cyber space. It is challenging primarily due to the dynamic nature of the methodology used to fake the review. There are several aspects to be considered when analyzing reviews to classify them effective into genuine and fake. Sentiment analysis, opinion mining and intend mining are fields of research that try to accomplish the goal through Natural Language Processing of the text content of the review.  In this paper, an approach that uses the review ratings evaluated along a timeline is presented. An Amazon dataset comprising of ratings indicated for a wide range of products was used for the analysis presented here. The analysis of the ratings was carried out for an electronic product over a period of six years.  The computed average rating helps to identify linear classifiers that define solution boundaries within the dataspace. This enables a product specific classification of review ratings and suitable recommendations can also be generated automatically. The paper explains a methodology to evaluate the average product ratings over time and presents the research outcomes using a novel classification tool. The proposed approach helps to determine the optimal point to distinguish between fake and genuine ratings for each product.    Index Terms: Fake reviews, Fake Ratings, Product Ratings, Online Shopping, Amazon Dataset.

scholarly journals Higher performance and lower complexity turbo decoding scheme for 4G-LTE using unpunctured turbo trellis-coded modulation

2020 ◽  
Vol 18 (1) ◽  
pp. 351
Author(s):  
Elarbi Abderraouf ◽  
Abdesselam Bassou ◽  
Mohamed Rida Lahcene Rida Lahcene
Keyword(s):  
Mobile Networks ◽  
Channel Coding ◽  
Iterative Decoding ◽  
High Efficiency ◽  
Turbo Code ◽  
Mobile Network ◽  
Coded Modulation ◽  
Mobile Technologies ◽  
Trellis Coded Modulation ◽  
Trellis Coded

<p>Thanks to the success of smart phones and mobile-ready laptops, data traffic has recently grown exponentially, and the demand for mobile data has risen very dramatically. These requests in large capacity can only be satisfied by a high efficiency and a very good optimization of the infrastructures of the mobile networks, while taking into account the constraints which are the power, bandwidth and a limited complexity. The task of developing mobile technologies has also evolved from a national or regional focus to a complex and growing mission, supported by global standards development organizations such as 3GPP (3rd Group Partnership Project). Through this research, we present everything related to the simulation of the 4G mobile network system (LTE), which can provide high data flow with good quality, through three model channels known as (EPA, EVA, ETU). In this work we focus on the block ‘iterative decoding channel encoder’ in the LTE system, where the iterative channel coding called ‘Turbo-code’ (TC) is substituted by the iterative coding channel called ‘Unpunctured Turbo Trellis-coded Modulation’ (UTTCM). The simulation results showed that with less decoding complexities, UTTCM's LTE system gives good performance (in terms of BER).</p>

scholarly journals Speech for Content Creation

2011 ◽  
Vol 3 (2) ◽  
pp. 35-49
Author(s):  
Joseph Polifroni ◽  
Imre Kiss ◽  
Stephanie Seneff
Keyword(s):  
Language Processing ◽  
Speech Processing ◽  
Opinion Mining ◽  
Prototype System ◽  
Dialogue Systems ◽  
Ancillary Data ◽  
Spoken Dialogue Systems ◽  
Content Creation ◽  
Sentiment Detection ◽  
Restaurant Reviews

This paper proposes a paradigm for using speech to interact with computers, one that complements and extends traditional spoken dialogue systems: speech for content creation. The literature in automatic speech recognition (ASR), natural language processing (NLP), sentiment detection, and opinion mining is surveyed to argue that the time has come to use mobile devices to create content on-the-fly. Recent work in user modelling and recommender systems is examined to support the claim that using speech in this way can result in a useful interface to uniquely personalizable data. A data collection effort recently undertaken to help build a prototype system for spoken restaurant reviews is discussed. This vision critically depends on mobile technology, for enabling the creation of the content and for providing ancillary data to make its processing more relevant to individual users. This type of system can be of use where only limited speech processing is possible.

scholarly journals Network Amelioration, AI Automation and Future Integration in Wireless Networks

2021 ◽  
Vol 9 (8) ◽  
pp. 2607-2613
Author(s):  
Marzook Khatri
Keyword(s):  
Artificial Intelligence ◽  
Communication Networks ◽  
Mobile Networks ◽  
Network Virtualization ◽  
Mobile Network ◽  
Data Access ◽  
Detailed Comparison ◽  
Mobile Industry ◽  
Generation Network ◽  
Network Operations

Abstract: The deployment of 5G mobile communication networks is just getting started right now. There are numerous technologies available today, each capable of fulfilling activities such as enabling voice traffic via voice over IP (VoIP), providing broadband data access in mobile environments, and so on. However, there is a pressing need to implement technology that can bring all of these systems together into a single unified system. Because it is all about smoothly integrating terminals, networks, and applications, 8G presents a solution to this dilemma. In this work, an attempt is made to provide a study of various cellular technologies, such as 4G, 5G, 6G, 7G, and FG, as well as a detailed comparison between them. With the introduction of network virtualization and the implementation of 5G/IoT, mobile networks will become more complicated and offer more diverse services. This raises concerns about a considerable increase in the workload of network operations. Meanwhile, artificial intelligence (AI) is advancing rapidly and is projected to alleviate human resource shortages in a variety of industries. Similarly, the mobile industry is gaining traction in the application of artificial intelligence (AI) to network operations in order to improve the efficiency of mobile network operations. This paper will address the idea of using AI technology to network operations and will give various use examples to demonstrate that AI-driven network operations have a bright future. Keywords: 5G & 6G networks, Artificial Intelligence, Next generation network, Future Advancement.

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