Affect Analysis of Radical Contents on Web Forums Using SentiWordNet

The internet has become a major tool for communication, training, fundraising, media operations, and recruitment, and these processes often use web forums. This paper presents a model that was built using SentiWordNet, WordNet and NLTK to analyze selected web forums that included radical content. SentiWordNet is a lexical resource for supporting opinion mining by assigning a positivity score and a negativity score to each WordNet. The approaches of the model measure and identify sentiment polarity and affect the intensity of that which appears in the web forum. The results show that SentiWordNet can be used for analyzing sentences that appear in web forums

Sentiment Analysis Of Web Forums: Comparison Between SentiWordNet And SentiStrength

Internet has become a major tool for communication, training, fundraising, media operations, and recruitment, and these processes often use web forums. This paper intended to find suitable technique for analysing selected web forums that included radical content by presenting a comparison between SentiWordNet and SentiStrength. SentiWordNet is a lexical resource for supporting opinion mining by assigning a positivity score and a negativity score to each WordNet. SentiStrength is a technique that was developed from comments on MySpace. It uses human-designed lexical and emotional terms with a set of amplification, diminishing and negation rules. The results have been presented and discussed

Some security issues for web based frameworks

This report investigates whether a vulnerability found in one web framework may be used to find a vulnerability in a different web framework. To test this hypothesis, several open source applications were installed in a secure test environment together with security analysis tools. Each one of the applications were developed using a different software framework. The results show that a vulnerability identified in one framework can often be used to find similar vulnerabilities in other frameworks. Crosssite scripting security issues are the most likely to succeed when being applied to more than one framework

Some Potential Issues with the Security of HTML5 IndexedDB

The new HTML5 standard provides much more access to client resources, such as user location and local data storage. Unfortunately, this greater access may create new security risks that potentially can yield new threats to user privacy and web attacks. One of these security risks lies with the HTML5 client-side database. It appears that data stored on the client file system is unencrypted. Therefore, any stored data might be at risk of exposure. This paper explains and performs a security investigation into how the data is stored on client local file systems. The investigation was undertaken using Firefox and Chrome web browsers, and Encase (a computer forensic tool), was used to examine the stored data. This paper describes how the data can be retrieved after an application deletes the client side database. Finally, based on our findings, we propose a solution to correct any potential issues and security risks, and recommend ways to store data securely on local file systems

Sentiment Analysis: State of the Art

We present the state of art in sentiment analysis which covers the purpose of sentiment analysis, levels of sentiment analysis and processes that could be used to measure polarity and classify labels. Moreover, brief details about some resources of sentiment analysis are included

TJP: using Twitter to analyze the polarity of contexts

This paper presents our system, TJP, whic

An Application of Sentiment Analysis Techniques to Determine Public Opinion in Social Media

This paper describes a prototype application that gathers textual data from the microblogging platform Twitter and carries out sentiment analysis to determine the polarity and subjectivity in relation to Brexit, the UK´ s exit from the European Union. The design, implementation and testing of the developed prototype will be discussed and an experimental evaluation of the product described. Specifically we provide insight into how events affect public opinion and how sentiment and public mood may be gathered from textual twitter data and propose this as an alternative to opinion polls. Traditional approaches to opinion polling face growing challenges in capturing the public mood. Small sample response and the time it takes to capture swings in public opinion make it difficult to provide accurate data for the political process. With over 500 million daily messages posted worldwide, the social media platform Twitter is an untapped resource of information. Users post short real time messages views and opinions on many topics, often signed with a ‘#hashtag’ to classify and document the subject matter in discussion. In this paper we apply automated sentiment analysis methods to tweets giving a measure of public support or hostility to a topic (‘Brexit’). The data were collected during several periods to determine changes in opinion. Using machine learning techniques we show that changes in opinion were also related to external events. Limitations of the method are that age, location and education are confounding factors where Twitter users over represent a young, urban public. However, the economic advantage of the method over real-time telephone polling are considerable

Knowledge Representation with Ontologies: The Present and Future

Recently, we have seen an explosion of interest in ontologies as artifacts to represent human knowledge and as critical components in knowledge management, the semantic Web, business-to-business applications, and several other application areas. Various research communities commonly assume that ontologies are the appropriate modeling structure for representing knowledge. However, little discussion has occurred regarding the actual range of knowledge an ontology can successfully represent

User activity pattern analysis in Telecare Data

Telecare is the use of devices installed in homes to deliver health and social care to the elderly and infirm. The aim of this paper is to identify patterns of use for different devices and associations between them. The data were provided by a telecare call centre in the North East of England. Using statistical analysis and machine learning, we analysed the relationships between users’ characteristics and device activations. We applied association rules and decision trees for the event analysis and our targeted projection pursuit technique was used for the user-event modelling. This study reveals that there is a strong association between users’ ages and activations, i.e., different age group users exhibit different activation patterns. In addition, a focused analysis on the users with mental health issues reveals that the older users with memory problems who live alone are likely to make more mistakes in using the devices than others. The patterns in the data can enable the telecare call centre to gain insight into their operations, and improve their effectiveness in several ways. This study also contributes to automatic analysis and support for decision making in the telecare industry