Multimodal Content Analysis for Effective Advertisements on YouTube

The rapid advances in e-commerce and Web 2.0 technologies have greatly increased the impact of commercial advertisements on the general public. As a key enabling technology, a multitude of recommender systems exists which analyzes user features and browsing patterns to recommend appealing advertisements to users. In this work, we seek to study the characteristics or attributes that characterize an effective advertisement and recommend a useful set of features to aid the designing and production processes of commercial advertisements. We analyze the temporal patterns from multimedia content of advertisement videos including auditory, visual and textual components, and study their individual roles and synergies in the success of an advertisement. The objective of this work is then to measure the effectiveness of an advertisement, and to recommend a useful set of features to advertisement designers to make it more successful and approachable to users. Our proposed framework employs the signal processing technique of cross modality feature learning where data streams from different components are employed to train separate neural network models and are then fused together to learn a shared representation. Subsequently, a neural network model trained on this joint feature embedding representation is utilized as a classifier to predict advertisement effectiveness. We validate our approach using subjective ratings from a dedicated user study, the sentiment strength of online viewer comments, and a viewer opinion metric of the ratio of the Likes and Views received by each advertisement from an online platform.Comment: 11 pages, 5 figures, ICDM 201

Multilingual fine-grained sentiment text classification of web content for advertising technology applications

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An iBeacon-Based Location-Aware Advertising System

The billboard market near its end for the development of electric commerce, especially with the maturing of online advertising systems. Traditional billboards demand innova- tive ways to attract and categorize users; and to direct them to desired advertisements. Location information is the prerequisite to pinpoint the users or customers. Location in- formation collection, GPS and cellular networks are better for outdoor navigation than indoor positioning. The emergence of Bluetooth Low Energy technology provides an effi- cient approach for indoor positioning services. In this thesis, we propose an iBeacon based location aware advertising system to make users acquire the desired advertisement. The system includes the server side, user side and billboard side. We will go into details about its concept, implementation and related background in the thesis. Finally, we analyzed the performance of the system and showed that the system is suitable for indoor environments

Optimal Radiometric Calibration for Camera-Display Communication

We present a novel method for communicating between a camera and display by embedding and recovering hidden and dynamic information within a displayed image. A handheld camera pointed at the display can receive not only the display image, but also the underlying message. These active scenes are fundamentally different from traditional passive scenes like QR codes because image formation is based on display emittance, not surface reflectance. Detecting and decoding the message requires careful photometric modeling for computational message recovery. Unlike standard watermarking and steganography methods that lie outside the domain of computer vision, our message recovery algorithm uses illumination to optically communicate hidden messages in real world scenes. The key innovation of our approach is an algorithm that performs simultaneous radiometric calibration and message recovery in one convex optimization problem. By modeling the photometry of the system using a camera-display transfer function (CDTF), we derive a physics-based kernel function for support vector machine classification. We demonstrate that our method of optimal online radiometric calibration (OORC) leads to an efficient and robust algorithm for computational messaging between nine commercial cameras and displays.Comment: 10 pages, Submitted to CVPR 201

Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning

This study investigates social media trends and proposes a buzz tweet classification method to explore the factors causing the buzz phenomenon on Twitter. It is difficult to identify the causes of the buzz phenomenon based solely on texts posted on Twitter. It is expected that by limiting the tweets to those with attached images and using the characteristics of the images and the relationships between the text and images, a more detailed analysis than that of with text-only tweets can be conducted. Therefore, an analysis method was devised based on a multi-task neural network that uses both the features extracted from the image and text as input and the buzz class (buzz/non-buzz) and the number of “likes (favorites)” and “retweets (RTs)” as output. The predictions made using a single feature of the text and image were compared with the predictions using a combination of multiple features. The differences between buzz and non-buzz features were analyzed based on the cosine similarity between the text and the image. The buzz class was correctly identified with a correctness rate of approximately 80% for all combinations of image and text features, with the combination of BERT and VGG16 providing the highest correctness rate

A comparison of machine learning approaches for detecting misogynistic Speech in urban dictionary

—Recent moves to consider misogyny as a hate crime have refocused efforts for owners of web properties to detect and remove misogynistic speech. This paper considers the use of deep learning techniques for detection of misogyny in Urban Dictionary, a crowdsourced online dictionary for slang words and phrases. We compare the performance of two deep learning techniques, Bi-LSTM and Bi-GRU, to detect misogynistic speech with the performance of more conventional machine learning techniques, logistic regression, Naive-Bayes classification, and Random Forest classification. We find that both deep learning techniques examined have greater accuracy in detecting misogyny in the Urban Dictionary than the other techniques examined. Dublin, Ireland [email protected] it was announced that the UK Law Commission would review whether misogynistic conduct should be treated as a hate crime [6]. Index Terms—misogyny, hate speech, recurrent neural networks, deep learning, LSTM, machine learning, urban dictionar

Social relationship analysis using state-of-the-art embeddings.

Detection of human relationships from their interactions on social media is a challenging problem with a wide range of applications in different areas, like targeted marketing, cyber-crime, fraud, defense, planning, and human resource, to name a few. All previous work in this area has only dealt with the most basic types of relationships. The proposed approach goes beyond the previous work to efficiently handle the hierarchy of social relationships. This article introduces a novel technique named Quantifiable Social Relationship (QSR) analysis for quantifying social relationships to analyze relationships between agents from their textual conversations. QSR uses cross-disciplinary techniques from computational linguistics and cognitive psychology to identify relationships. QSR utilizes sentiment and behavioral styles displayed in the conversations for mapping them onto level II relationship categories. Then, for identifying the level III relationship categories, QSR uses level II relationships, sentiments, interactions, and word embeddings as key features. QSR employs natural language processing techniques for feature engineering and state-of-the-art embeddings generated by word2vec, global vectors (glove), and bidirectional encoder representations from transformers (bert). QSR combines the intrinsic conversational features with word embeddings for classifying relationships. QSR achieves an accuracy of up to 89% for classifying relationship subtypes. The evaluation shows that QSR can accurately identify the hierarchical relationships between agents by extracting intrinsic and extrinsic features from textual conversations between agents

Aspect-Based Sentiment Analysis using Machine Learning and Deep Learning Approaches

Sentiment analysis (SA) is also known as opinion mining, it is the process of gathering and analyzing people's opinions about a particular service, good, or company on websites like Twitter, Facebook, Instagram, LinkedIn, and blogs, among other places. This article covers a thorough analysis of SA and its levels. This manuscript's main focus is on aspect-based SA, which helps manufacturing organizations make better decisions by examining consumers' viewpoints and opinions of their products. The many approaches and methods used in aspect-based sentiment analysis are covered in this review study (ABSA). The features associated with the aspects were manually drawn out in traditional methods, which made it a time-consuming and error-prone operation. Nevertheless, these restrictions may be overcome as artificial intelligence develops. Therefore, to increase the effectiveness of ABSA, researchers are increasingly using AI-based machine learning (ML) and deep learning (DL) techniques. Additionally, certain recently released ABSA approaches based on ML and DL are examined, contrasted, and based on this research, gaps in both methodologies are discovered. At the conclusion of this study, the difficulties that current ABSA models encounter are also emphasized, along with suggestions that can be made to improve the efficacy and precision of ABSA systems

Understanding the Transformation from E-commerce to S-commerce: Evidence, Path and Inspiration from China

With the rapid application of Web2.0 and the rise of social media, social-commerce has become a research hotspot in the field of marketing and information systems. This new business form which is driven by the user\u27s social interaction, is considered as a new paradigm in current e-commerce research field. However, current studies are mainly limited in the field of marketing, the law of s-commerce’s evolution and its impact on the traditional e-commerce is not been fully considered. In recent years, China’s s-commerce has developed rapidly and has not received enough research attention. This study selects Taobao.com and PingDuoDuo as typical cases to analyze the “network stream dilemma” of the traditional e-commerce platform and the solution in the s-commerce context. We conclude that traditional e-commerce platforms are evolving toward s-commerce driven by the change of network stream distribution mechanism