Topic Identification System to Filter Twitter Feeds

Twitter is a micro-blogging service where users publish messages of 140 characters. This simple feature makes Twitter the source for concise, instant and interesting information ranging from friends' updates to breaking news. However, a problem emerge when a user follows many accounts while interested in a subset of its content, which leads to overwhelming tweets he is not interested in receiving. We propose a solution to this problem by filtering incoming tweets based on the user's interests, which is accomplished through a classifier. The proposed classifier system categorizes tweets into generic classes like Entertainment, Health, Sport, News, Food, Technology and Health. This paper describes the creation and evaluation of the classifier until 89% accuracy obtained

Mining User Interests from Social Media

Social media users readily share their preferences, life events, sentiment and opinions, and implicitly signal their thoughts, feelings, and psychological behavior. This makes social media a viable source of information to accurately and effectively mine users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. In this tutorial, we cover five important aspects related to the effective mining of user interests: (1) the foundations of social user interest modeling, such as information sources, various types of representation models and temporal features, (2) techniques that have been adopted or proposed for mining user interests, (3) different evaluation methodologies and benchmark datasets, (4) different applications that have been taking advantage of user interest mining from social media platforms, and (5) existing challenges, open research questions and exciting opportunities for further work

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

Inferring user interests in microblogging social networks: a survey

With the growing popularity of microblogging services such as Twitter in recent years, an increasing number of users are using these services in their daily lives. The huge volume of information generated by users raises new opportunities in various applications and areas. Inferring user interests plays a significant role in providing personalized recommendations on microblogging services, and also on third-party applications providing social logins via these services, especially in cold-start situations. In this survey, we review user modeling strategies with respect to inferring user interests from previous studies. To this end, we focus on four dimensions of inferring user interest profiles: (1) data collection, (2) representation of user interest profiles, (3) construction and enhancement of user interest profiles, and (4) the evaluation of the constructed profiles. Through this survey, we aim to provide an overview of state-of-the-art user modeling strategies for inferring user interest profiles on microblogging social networks with respect to the four dimensions. For each dimension, we review and summarize previous studies based on specified criteria. Finally, we discuss some challenges and opportunities for future work in this research domain

Network-aware recommendations in online social networks

Along with the rapid increase of using social networks sites such as Twitter, a massive number of tweets published every day which generally affect the users decision to forward what they receive of information, and result in making them feel overwhelmed with this information. Then, it is important for this services to help the users not lose their focus from what is close to their interests, and to find potentially interesting tweets. The problem that can occur in this case is called information overload, where an individual will encounter too much information in a short time period. For instance, in Twitter, the user can see a large number of tweets posted by her followees. To sort out this issue, recommender systems are used to give contents that match the user's needs. This thesis presents a tweet-recommendation approach aiming at proposing novel tweets to users and achieving improvement over baseline. For this reason, we propose to exploit network, content, and retweet analyses for making recommendations of tweets. The main objective of this research is to recommend tweets that are unseen by the user (i.e., they do not appear in the user timeline) because nobody in her social circles published or retweeted them. To achieve this goal, we create the user's ego-network up to depth two and apply the transitivity property of the \emph{friends-of-friends} relationship to determine interesting recommendations. After this step, we apply cosine similarity and Jaccard distance as similarity measures for the candidate tweets obtained from the network analysis using bigrams. We also count the mutual retweets between the ego user and candidate users as a measure of shared similar tastes. The values of these features are compared together for each of the candidate tweets using pairwise comparisons in order to determine interesting recommendations that are ranked to best match the user's interests. Experimental results demonstrate through a real user study that our approach improves the state-of-the-art technique. In addition to the efficiency of our approach in finding relevant contents, it is also characterized by the fact of providing novel tweets, which solves the over-specialization challenge or serendipity problem that appears when using content-based recommender systems as a stand alone approach of recommendation

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF