A Systematic Review of Existing Data Mining Approaches Envisioned for Knowledge Discovery from Multimedia

The extensive use of multimedia technologies extended the applicability of information technology to a large extent which results enormous generation of complex multimedia contents over the internet. Therefore the number of multimedia contents available to the user is also exponentially increasing. In this digital era of the cloud-enabled Internet of Things (IoT), analysis of complex video and image data plays a crucial role.It aims to extract meaningful information as the distributed storages and processing elements within a bandwidth constraint network seek optimal solutions to increase the throughput along with an optimal trade-off between computational complexity and power consumption. However, due to complex characteristics of visual patterns and variations in video frames, it is not a trivial task to discover meaningful information and correlation. Hence, data mining has emerged as a field which has diverse aspects presently in extracting meaningful hidden patterns from the complex image and video data considering different pattern classification approach. The study mostly investigates the existing data-mining tools and their performance metric for the purpose of reviewing this research track.It also highlights the relationship between frequent patterns and discriminativefeatures associated with a video object. Finally, the study addresses the existing research issues to strengthen up the future direction of research towards video analytics and pattern recognition

Sentiment analysis for TV show popularity prediction: case of Nation Media Group’s NTV

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore UniversityMedia-houses play a vital role in informing the masses on the issues that matter. They are also a source of entertainment for many households. In Kenya, the public depends on media largely for entertainment and educational purposes. However, many media-houses find it difficult to make decisions on what the viewers actually wish to watch. This makes the media-houses to be in the dark, unaware of what viewers want and making decisions based on perceptions rather than data. Most of the analytic tools used by media-houses in Kenya are used to provide insights on website-related activities such as site visits, number of article reads and read-depths. This is not a good way of measuring popularity and does not create a true reflection of how an audience perceives a given product. In this study, we propose a predictive model that uses sentiment analysis to determine the popularity of a given TV show. This enables accurate decisions to be made based on the viewership trends over a specific period of time. Natural Language Processing is used to perform sentiment analysis on tweets derived from Twitter. This solution involved tweets being derived from the social site Twitter through the use of the Twitter API. Once fetched, the tweets had their polarity measured through the use of a lexicon dictionary in order to remove neutral tweets. These tweets were then be labelled as either positive or negative using the Support Vector Machine classifier. Then the overall popularity score of a movie was calculated. The solution was able to not only show the polarity of derived tweets, but also assign overall popularity scores showing how positive or negative a TV show is

Exploring The Effects Of Multimedia Content On A Question And Answer System

Online Question and Answer (Q&A) websites have been a part of the Internet for many years, the most well-known being Yahoo! Answers. These websites allow us to ask and answer questions with other Internet users around the world, utilizing one of the Internet\u27s greatest strengths: information sharing. In the past, this sharing was restricted to primarily text-based content, and previous research has been almost solely devoted to these text-based Q&A systems. This research includes topics such as improving text-based question quality, and methods for finding the best text-based answer. Now, new Q&A systems such as Jelly have recently been released that utilize multimedia, including images, audio, or video for questions and/or answers. These new multimedia features were not part of previous text-based research, and the objective of this project is to fill that research void: how does the use of multimedia in a question affect the odds of receiving the correct answer? Our findings will not only affect this previous Q&A research on question quality and forwarding, but also reveal new security and privacy concerns. To perform our research, we created several different types of multimedia questions, spanning many different topics. We then studied how users answered each question, using our custom MultiQuery website to facilitate the experiment. This website is unique to Q&A in that it allows for many different multimedia question types, not simply text. Once the experiment was completed, we analyzed the results and determined if and to what degree multimedia use in a question improves or worsens the quality of answers; both overall and for each specific topic of questions. We found that multimedia did in fact have a beneficial effect, especially with images, showing a higher answer rating and correctness percentage for most multimedia types. We also found many security and privacy risks from integrating multimedia into a Q&A website, including loss of privacy through image sharing and voice recognition, as well as security dangers to Q&A system owners through illegal, malicious, or explicit uploaded multimedia content. Our analysis and discussion will help future multimedia Q&A systems as they seek to implement the most effective forms of multimedia, will aid future research into multimedia question quality and forwarding algorithms, and will provide recommendations for safer Q&A security practices that account for multimedia

Influence of social media on performance of movies

"May 2014."Thesis advisor: Dr. Wenjun Zeng.Includes bibliographical references (pages 51-53)

Taste and the algorithm

Today, a consistent part of our everyday interaction with art and aesthetic artefacts occurs through digital media, and our preferences and choices are systematically tracked and analyzed by algorithms in ways that are far from transparent. Our consumption is constantly documented, and then, we are fed back through tailored information. We are therefore witnessing the emergence of a complex interrelation between our aesthetic choices, their digital elaboration, and also the production of content and the dynamics of creative processes. All are involved in a process of mutual influences, and are partially determined by the invisible guiding hand of algorithms. With regard to this topic, this paper will introduce some key issues concerning the role of algorithms in aesthetic domains, such as taste detection and formation, cultural consumption and production, and showing how aesthetics can contribute to the ongoing debate about the impact of today’s “algorithmic culture”

Integration of a recommender system into an online video streaming platform

The ultimate goal of this project is to develop a recommender system for the SmartVideo platform. The platform streams different content of local channels for the Grand Est Region of France to a large public. So, we aim to propose a solution to alleviate the data representation and data collection issue of recommender systems by adopting and adjusting the xAPI standard to fit our case of study and to be able to represent our usage data in a formal and consistent format. Then, we will propose and implement a bunch of recommendation algorithms that we are going to test in order to evaluate our developed recommender system.Le but ultime de ce projet est de développer un système de recommandation dédié à la plateforme SmartVideo de diffusion de vidéo en ligne. En effet, la plateforme met à disposition diverses contenus des chaînes locales de la région Grand Est du France. Alors, nous allons présenter une solution pour alléger le problème de représentation et de collecte de données d’usages par adopter et ajuster le standard xAPI pour représenter et collecter les données de façon simple et formelle. Ensuite, nous allons proposer et implanter des algorithmes de recommandation que nous allons les tester pour évaluer notre système de recommandation