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

User Emotion Identification in Twitter Using Specific Features: Hashtag, Emoji, Emoticon, and Adjective Term

Twitter is a social media application, which can give a sign for identifying user emotion. Identification of user emotion can be utilized in commercial domain, health, politic, and security problems. The problem of emotion identification in twit is the unstructured short text messages which lead the difficulty to figure out main features. In this paper, we propose a new framework for identifying the tendency of user emotions using specific features, i.e. hashtag, emoji, emoticon, and adjective term. Preprocessing is applied in the first phase, and then user emotions are identified by means of classification method using kNN. The proposed method can achieve good results, near ground truth, with accuracy of 92%

Real-time expressive internet communications

This research work "Real-time Expressive Internet Communications" focuses on two subjects: One is the investigation of methods of automatic emotion detection and visualisation under real-time Internet communication environment, the other is the analysis of the influences of presenting visualised emotion expressivei mages to Internet users. To detect emotion within Internet communication, the emotion communication process over the Internet needs to be examined. An emotion momentum theory was developed to illustrate the emotion communication process over the Internet communication. It is argued in this theory that an Internet user is within a certain emotion state, the emotion state is changeable by internal and external stimulus (e.g. a received chat message) and time; stimulus duration and stimulus intensity are the major factors influencing the emotion state. The emotion momentum theory divides the emotions expressed in Internet communication into three dimensions: emotion category, intensity and duration. The emotion momentum theory was implemented within a prototype emotion extraction engine. The emotion extraction engine can analyse input text in an Internet chat environment, detect and extract the emotion being communicated, and deliver the parameters to invoke an appropriate expressive image on screen to the every communicating user's display. A set of experiments were carried out to test the speed and the accuracy of the emotion extraction engine. The results of the experiments demonstrated an acceptable performance of the emotion extraction engine. The next step of this study was to design and implement an expressive image generator that generates expressive images from a single neutral facial image. Generated facial images are classified into six categories, and for each category, three different intensities were achieved. Users need to define only six control points and three control shapes to synthesise all the expressive images and a set of experiments were carried out to test the quality of the synthesised images. The experiment results demonstrated an acceptable recognition rate of the generated facial expression images. With the emotion extraction engine and the expressive image generator,a test platform was created to evaluate the influences of emotion visualisation in the Internet communication context. The results of a series of experiments demonstratedthat emotion visualisation can enhancethe users' perceived performance and their satisfaction with the interfaces. The contributions to knowledge fall into four main areas; firstly, the emotion momentum theory that is proposed to illustrate the emotion communication process over the Internet; secondly, the innovations built into an emotion extraction engine, which senses emotional feelings from textual messages input by Internet users; thirdly, the innovations built into the expressive image generator, which synthesises facial expressions using a fast approach with a user friendly interface; and fourthly, the identification of the influence that the visualisation of emotion has on human computer interaction

TJP: using Twitter to analyze the polarity of contexts

This paper presents our system, TJP, whic

Real-time expressive Internet communications

This research work "Real-time Expressive Internet Communications" focuses on two subjects: One is the investigation of methods of automatic emotion detection and visualisation under real-time Internet communication environment, the other is the analysis of the influences of presenting visualised emotion expressivei mages to Internet users. To detect emotion within Internet communication, the emotion communication process over the Internet needs to be examined. An emotion momentum theory was developed to illustrate the emotion communication process over the Internet communication. It is argued in this theory that an Internet user is within a certain emotion state, the emotion state is changeable by internal and external stimulus (e.g. a received chat message) and time; stimulus duration and stimulus intensity are the major factors influencing the emotion state. The emotion momentum theory divides the emotions expressed in Internet communication into three dimensions: emotion category, intensity and duration. The emotion momentum theory was implemented within a prototype emotion extraction engine. The emotion extraction engine can analyse input text in an Internet chat environment, detect and extract the emotion being communicated, and deliver the parameters to invoke an appropriate expressive image on screen to the every communicating user's display. A set of experiments were carried out to test the speed and the accuracy of the emotion extraction engine. The results of the experiments demonstrated an acceptable performance of the emotion extraction engine. The next step of this study was to design and implement an expressive image generator that generates expressive images from a single neutral facial image. Generated facial images are classified into six categories, and for each category, three different intensities were achieved. Users need to define only six control points and three control shapes to synthesise all the expressive images and a set of experiments were carried out to test the quality of the synthesised images. The experiment results demonstrated an acceptable recognition rate of the generated facial expression images. With the emotion extraction engine and the expressive image generator,a test platform was created to evaluate the influences of emotion visualisation in the Internet communication context. The results of a series of experiments demonstratedthat emotion visualisation can enhancethe users' perceived performance and their satisfaction with the interfaces. The contributions to knowledge fall into four main areas; firstly, the emotion momentum theory that is proposed to illustrate the emotion communication process over the Internet; secondly, the innovations built into an emotion extraction engine, which senses emotional feelings from textual messages input by Internet users; thirdly, the innovations built into the expressive image generator, which synthesises facial expressions using a fast approach with a user friendly interface; and fourthly, the identification of the influence that the visualisation of emotion has on human computer interaction.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

PERSONALITY TYPE AND TRANSLATION PERFORMANCE OF PERSIAN TRANSLATOR TRAINEES

The study investigated the relationship between the personality typology of a sample of Iranian translation students and their translation quality in terms of expressive, appellative, and informative text types. The study also attempted to identify the personality types that can perform better in English to Persian translation of the three text types. For that purpose, the personality type and the translation quality of the participants was assessed using Myers-Briggs Type Indicator (MBTI) personality test and translation quality assessment (TQA), respectively. The analysis of the data revealed that the personality type of the participants seemed relevant to the translation quality of all the text types. The translation quality of the participants with intuitive and thinking types was significantly better than the sensing type counterparts in translating expressive texts. The participants with intuitive and feeling types also performed better than their counterparts with sensing type in translation of the informative text. Moreover, the participants with intuitive, feeling, and thinking personality types performed more successfully than the participants with sensing type in translation of the appellative text. The findings of the study are discussed in light of the existing research literature.

Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence