scholarly journals Rethinking Defeasible Reasoning: A Scalable Approach

2020 ◽  
Vol 20 (4) ◽  
pp. 552-586
Author(s):  
MICHAEL J. MAHER ◽  
ILIAS TACHMAZIDIS ◽  
GRIGORIS ANTONIOU ◽  
STEPHEN WADE ◽  
LONG CHENG
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Sensor Networks ◽  
Experimental Results ◽  
Defeasible Reasoning ◽  
Social Media Analytics ◽  
Technological Advances ◽  
Large Numbers ◽  
Complex Knowledge ◽  
The Web

AbstractRecent technological advances have led to unprecedented amounts of generated data that originate from the Web, sensor networks, and social media. Analytics in terms of defeasible reasoning – for example, for decision making – could provide richer knowledge of the underlying domain. Traditionally, defeasible reasoning has focused on complex knowledge structures over small to medium amounts of data, but recent research efforts have attempted to parallelize the reasoning process over theories with large numbers of facts. Such work has shown that traditional defeasible logics come with overheads that limit scalability. In this work, we design a new logic for defeasible reasoning, thus ensuring scalability by design. We establish several properties of the logic, including its relation to existing defeasible logics. Our experimental results indicate that our approach is indeed scalable and defeasible reasoning can be applied to billions of facts.

scholarly journals SentiProdBR: Building Domain-Specific Sentiment Lexicons for the Portuguese Language

2021 ◽  
Author(s):  
Tiago de Melo
Keyword(s):  
Decision Making ◽  
Sentiment Analysis ◽  
Online Reviews ◽  
Bayes Theorem ◽  
Experimental Results ◽  
Product Categories ◽  
Domain Specific ◽  
Alternative Approaches ◽  
The Web

Online reviews are readily available on the Web and widely used for decision-making. However, only a few studies on Portuguese sentiment analysis are reported due to the lack of resources including domain-specific sentiment lexical collections. In this paper, we present an effective methodology using probabilities of the Bayes’ Theorem for building a set of lexicons, called SentiProdBR, for 10 different product categories for the Portuguese language. Experimental results indicate that our methodology significantly outperforms several alternative approaches of building domain-specific sentiment lexicons.

scholarly journals Machine Learning Approach for User Accounts Identification with Unwanted Information and data

2018 ◽  
Vol 2 (3) ◽  
pp. 119-127 ◽  
Author(s):  
Abhishek Kumar ◽  
TVM SAIRAM
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Online Social Networks ◽  
Social Media Analytics ◽  
Learning Approach ◽  
Efficient Manner ◽  
Implementation Phase ◽  
Large Numbers ◽  
The Social ◽  
Machine Learning Approach

Machine Learning used for many real-time issues in many organizations and the purpose of social media analytics machine learning models are used most prominently and to identify the genuine accounts and the information in the social media we are here with a new pattern of identification. In this pattern of the model, we are proposing some words which are hidden to identify the accounts with fake data and the some of the steps we are proposing will help to identify the fake and unwanted accounts in Facebook in an efficient manner. Clustering in machine learning will be used, and before that, we are proposing a suitable architecture and the process flow which can identify the fake and suspicious accounts in the social media. This article will be on machine learning implementations and will be working on OSN (online social networks). Our work will be more on Facebook which is maintaining more amount of accounts and identifying which are overruling the rules on privacy and protection of the user content. Machine learning supervised models will be used for text classification, and CNN of unsupervised learning performs the image classification, and the explanation will be given in the implementation phase. There are large numbers of algorithms we can consider for machine learning implementations in the social networking and here we considered mainly on CNN because of having the feasibility of implementation in different rules and we can eliminate the features we don’t need. Feature extraction is quite simple using CNN.

scholarly journals Analisis Waktu Terbaik untuk Menerbitkan Konten di Instagram untuk Menjangkau Audiens

2020 ◽  
Vol 24 (1) ◽  
pp. 59-70
Author(s):  
Adri Priadana ◽  
Aris Wahyu Murdiyanto
Keyword(s):  
Social Media ◽  
Service Providers ◽  
Complex Problem ◽  
Social Media Analytics ◽  
Promotional Material ◽  
Optimal Response ◽  
Web Scraping ◽  
Open Service ◽  
The Right ◽  
The Web

Instagram is a social media that allows us to easily promote products where one of them is done by publishing promotional content. However, posting promotional material at the right time to get an optimal response from the audience is a complex problem. This study aims to analyze the best publishing time to publish promotional content from 10 open trip service provider accounts on the Instagram platform. Researchers use the web scraping method to extract data from Instagram accounts and the aggregation, ordering, and selecting methods to analyze the best time. The basis used to determine the best time is the number of likes and comments on all posts. This research has succeeded in extracting Instagram's web data and analyzing post data from several Instagram accounts of open trip service providers. The results of this study indicate that each account has a different best time to publish content. For example, the best time to post content from an Instagram @hvtrip account is Friday between 20:00 and 20.59. The study can be used as a recommendation for Instagram account holders of open trip service providers regarding the best time to publish promotional content on Instagram to reach an optimal audience. Of course, this is not limited to Instagram accounts open service providers only. Keywords: social media analytics, Instagram data, marketing, open trip services, the best time   ABSTRAK Instagram merupakan salah satu media sosial yang memungkinkan kita untuk mempromosikan produk dengan mudah dimana salah satunya dilakukan dengan cara menerbitkan konten promosi. Akan tetapi, penerbitan konten prosmosi pada waktu yang tepat untuk mendapatkan tanggapan dari audiens secara optimal merupakan masalah yang kompleks. Penelitian ini bertujuan menganalisis waktu penerbitan terbaik untuk menerbitkan konten promosi dari 10 akun penyedia jasa open trip pada platform Instagram. Peneliti menggunakan metode web scraping untuk mengekstrak data dari akun Instagram dan metode aggregation, ordering, dan selecting untuk menganalisis waktu terbaik. Dasar yang digunakan untuk menentukan waktu terbaik adalah jumlah suka dan komentar pada semua post. Penelitian ini telah berhasil mengekstraksi data web Instagram dan melakukan analisis data post dari beberapa akun Instagram penyedia jasa open trip. Hasil penelitian ini menunjukkan bahwa setiap akun memiliki waktu terbaik yang berbeda-beda untuk menerbitkan konten. Sebagai contoh, waktu terbaik untuk menerbitkan konten dari akun Instagram @hvtrip adalah hari Jumat antara jam 20.00 sampai jam 20.59. Hasil dari penelitian ini dapat dijadikan sebagai sebuah rekomendasi bagi pemilik akun Instagram penyedia jasa open trip mengenai waktu terbaik untuk menerbitkan konten promosi pada Instagram untuk menjangkau audiens secara optimal. Tentunya, hal ini tidak terbatas pada akun Instagram penyedia jasa open trip saja. Kata kunci: analisis media sosial, data Instagram, pemasaran, jasa open trip, waktu terbaik

scholarly journals DAMN: Defeasible Reasoning Tool for Multi-Agent Reasoning

2020 ◽  
Vol 34 (09) ◽  
pp. 13612-13613
Author(s):  
Abdelraouf Hecham ◽  
Madalina Croitoru ◽  
Pierre Bisquert
Keyword(s):  
Decision Making ◽  
Knowledge Base ◽  
Defeasible Reasoning ◽  
Agent Collaboration ◽  
Multi Agent ◽  
The Web

This demonstration paper introduces DAMN: a defeasible reasoning platform available on the web. It is geared towards decision making where each agent has its own knowledge base that can be combined with other agents to detect and visualize conflicts and potentially solve them using a semantics. It allows the use of different defeasible reasoning semantics (ambiguity blocking/propagating with or without team defeat) and integrates agent collaboration and visualization features.

Sentiment Analysis of Game Review Using Machine Learning in a Hadoop Ecosystem

2020 ◽  
pp. 145-165
Author(s):  
Arvind Panwar ◽  
Vishal Bhatnagar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Experimental Work ◽  
Electronic Device ◽  
Social Media Analytics ◽  
Social Game ◽  
Hadoop Ecosystem ◽  
Global Platform ◽  
The Web

Internet, & more unambiguously the creation of WWW in the early 1990s, helped people to build an interconnected global platform where information can be stored, shared, and consumed by anyone with an electronic device which has the ability to connect to the Web. This provides a way of putting together lots of information, ideas, and opinion. An interactive platform was born to post content, messages, and opinions under one roof, and the platform is known as social media. Social media has acquired massive popularity and importance that why today almost everyone can't stay away from it. Social media is not only a medium for people to express their thoughts, moreover, but it is also a very powerful tool which can be used by businesses to focus on new and existing customers and increase profit with the help of social media analytics. This paper starts with a discussion on social media with its significance & pitfalls. Later on, this paper presents a brief introduction of sentiment analysis in social media and give an experimental work on sentiment analysis in a social game review.

Tracking via Geotagged Social Media Data

2020 ◽  
Author(s):  
Anne Hardy
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Single Point ◽  
Application Programming Interface ◽  
Continuous Location ◽  
Social Media Data ◽  
Large Numbers ◽  
Application Programming ◽  
Media Data ◽  
Programming Interface

Over the past twenty years, social media has changed the ways in which we plan, travel and reflect on our travels. Tourists use social media while travelling to stay in touch with friends and family, enhance their social status (Guo et al., 2015); and assist others with decision making (Xiang and Gretzel, 2010; Yoo and Gretzel, 2010). They also use it to report back to their friends and family where they are. This can be done using a geotag function that provides a location for where a post is made. While little is known about why tourists choose to geotag their social media posts, Chung and Lee (2016) suggest that geotags may be used in an altruistic manner by tourists, in order to provide information, and because they elicit a sense of anticipated reward. What is known, however, is that the function offers researchers the ability to understand where tourists travel. There are two types of geotagged social media data. The first of these is discussed in this chapter and may be defined as single point geo-referenced data – geotagged social media posts whose release is chosen by the user. This includes data gathered from social media apps such as Facebook, Instagram, Twitter and WeiChat. The method of obtaining this data involves the collation of large numbers of discrete geotagged updates or photographs. Data can be collated via an application programming interface (API) provided by the app developer to researchers, by automated data scraping via computer programs, perhaps written in Python, or manually by researchers. The second type of data is continuous location-based data from applications that are designed to track movement constantly, such as Strava or MyFitnessPal. Tracking methods using this continuous location-based data are discussed in detail in the following chapter.

Influence of Social Media Analytics on Online Food Delivery Systems

2020 ◽  
Vol 11 (3) ◽  
pp. 1-21
Author(s):  
Ravindra Kumar Singh ◽  
Harsh Kumar Verma
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Food Delivery ◽  
Social Media Analytics ◽  
Consumer Behavior Analysis ◽  
Brand Reputation ◽  
Metropolitan Cities ◽  
Social Media Platforms ◽  
Delivery Options ◽  
Significant Attention

Online food delivery applications have gained significant attention in the metropolitan cities by diminishing the burden of traveling and waiting time by offering online food delivery options for various dishes from many such restaurants. Users enjoy these services and share their experiences and opinions on social media platforms that impact the trust of customers and change their purchasing habits. This drastic revolution of user activities is an opportunity for targeted social marketing. This research is based on Twitter's data and aimed to identify the influence of social media in food delivery e-commerce businesses including decision making, marketing strategy, consumer behavior analysis, and improving brand reputation. In this article, the authors proposed an Apache Spark-based social media analytics framework to process the tweets in real time to identify the influences of generated insights on e-commerce decision making. The experimental analysis highlighted the exponentially grown influence of social media in food delivery e-commerce portals in past years.

scholarly journals Efficient Computation of the Well-Founded Semantics over Big Data

2014 ◽  
Vol 14 (4-5) ◽  
pp. 445-459 ◽  
Author(s):  
ILIAS TACHMAZIDIS ◽  
GRIGORIS ANTONIOU ◽  
WOLFGANG FABER
Keyword(s):  
Social Media ◽  
Big Data ◽  
Logic Programming ◽  
Large Scale ◽  
Nonmonotonic Reasoning ◽  
Efficient Computation ◽  
Mapreduce Framework ◽  
The Face ◽  
Complex Knowledge ◽  
The Web

AbstractData originating from the Web, sensor readings and social media result in increasingly huge datasets. The so called Big Data comes with new scientific and technological challenges while creating new opportunities, hence the increasing interest in academia and industry. Traditionally, logic programming has focused on complex knowledge structures/programs, so the question arises whether and how it can work in the face of Big Data. In this paper, we examine how the well-founded semantics can process huge amounts of data through mass parallelization. More specifically, we propose and evaluate a parallel approach using the MapReduce framework. Our experimental results indicate that our approach is scalable and that well-founded semantics can be applied to billions of facts. To the best of our knowledge, this is the first work that addresses large scale nonmonotonic reasoning without the restriction of stratification for predicates of arbitrary arity.

Sign in / Sign up

Export Citation Format

Share Document