Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture

The World Wide Web holds a wealth of information in the form of unstructured texts such as customer reviews for products, events and more. By extracting and analyzing the expressed opinions in customer reviews in a fine-grained way, valuable opportunities and insights for customers and businesses can be gained. We propose a neural network based system to address the task of Aspect-Based Sentiment Analysis to compete in Task 2 of the ESWC-2016 Challenge on Semantic Sentiment Analysis. Our proposed architecture divides the task in two subtasks: aspect term extraction and aspect-specific sentiment extraction. This approach is flexible in that it allows to address each subtask independently. As a first step, a recurrent neural network is used to extract aspects from a text by framing the problem as a sequence labeling task. In a second step, a recurrent network processes each extracted aspect with respect to its context and predicts a sentiment label. The system uses pretrained semantic word embedding features which we experimentally enhance with semantic knowledge extracted from WordNet. Further features extracted from SenticNet prove to be beneficial for the extraction of sentiment labels. As the best performing system in its category, our proposed system proves to be an effective approach for the Aspect-Based Sentiment Analysis

Cross-Lingual and Low-Resource Sentiment Analysis

Identifying sentiment in a low-resource language is essential for understanding opinions internationally and for responding to the urgent needs of locals affected by disaster incidents in different world regions. While tools and resources for recognizing sentiment in high-resource languages are plentiful, determining the most effective methods for achieving this task in a low-resource language which lacks annotated data is still an open research question. Most existing approaches for cross-lingual sentiment analysis to date have relied on high-resource machine translation systems, large amounts of parallel data, or resources only available for Indo-European languages. This work presents methods, resources, and strategies for identifying sentiment cross-lingually in a low-resource language. We introduce a cross-lingual sentiment model which can be trained on a high-resource language and applied directly to a low-resource language. The model offers the feature of lexicalizing the training data using a bilingual dictionary, but can perform well without any translation into the target language. Through an extensive experimental analysis, evaluated on 17 target languages, we show that the model performs well with bilingual word vectors pre-trained on an appropriate translation corpus. We compare in-genre and in-domain parallel corpora, out-of-domain parallel corpora, in-domain comparable corpora, and monolingual corpora, and show that a relatively small, in-domain parallel corpus works best as a transfer medium if it is available. We describe the conditions under which other resources and embedding generation methods are successful, and these include our strategies for leveraging in-domain comparable corpora for cross-lingual sentiment analysis. To enhance the ability of the cross-lingual model to identify sentiment in the target language, we present new feature representations for sentiment analysis that are incorporated in the cross-lingual model: bilingual sentiment embeddings that are used to create bilingual sentiment scores, and a method for updating the sentiment embeddings during training by lexicalization of the target language. This feature configuration works best for the largest number of target languages in both untargeted and targeted cross-lingual sentiment experiments. The cross-lingual model is studied further by evaluating the role of the source language, which has traditionally been assumed to be English. We build cross-lingual models using 15 source languages, including two non-European and non-Indo-European source languages: Arabic and Chinese. We show that language families play an important role in the performance of the model, as does the morphological complexity of the source language. In the last part of the work, we focus on sentiment analysis towards targets. We study Arabic as a representative morphologically complex language and develop models and morphological representation features for identifying entity targets and sentiment expressed towards them in Arabic open-domain text. Finally, we adapt our cross-lingual sentiment models for the detection of sentiment towards targets. Through cross-lingual experiments on Arabic and English, we demonstrate that our findings regarding resources, features, and language also hold true for the transfer of targeted sentiment

The German Immigration Debate and the Effects of News Coverage on Political Preferences

Eine umfangreiche Literatur zu Framing-Effekten legt nahe, dass Bürger nur über begrenzte politische Präferenzen verfügen. Wenn die öffentliche Meinung so offen für Einflussnahme ist, stellt sie ein wackliges Fundament für den demokratischen Prozess dar. Diese Dissertation stellt daher die Frage, wie sich vorherige experimentelle Erkenntnisse auf komplexe, reale Situationen übertragen lassen und ob Framing auch Wahlabsichten beeinflussen kann. Sie entwickelt eine Methode zur automatischen Identifizierung von Nachrichtenframes. Die Dissertation präsentiert Original- und Sekundärdaten und untersucht den Zusammenhang zwischen Nachrichten-Framing, Migrationseinstellungen und Wahlabsichten. Sie bietet einen Überblick über die Darstellung der Einwanderung in den deutschen Nachrichtenmedien und zeigt, dass weder die Aufmerksamkeit noch das Framing von Migration den Aufstieg der rechtsradikalen AfD erklären können. Anschließend nutzt sie eine Änderung in der Migrationsberichterstattung Deutschlands größter Boulevardzeitung, Bild, und zeigt begrenzte Auswirkungen auf politische Einstellungen und Wahlabsichten ihrer Leser auf. Das letzte empirische Kapitel präsentiert experimentelle Daten, die aufzeigen, dass Framing lediglich die Wahlabsichten eher uninformierter Bürger beeinflusst. Die Ergebnisse tragen zum besseren Verständnis von Framing-Effekten bei und legen nahe, dass Einstellungen von Bürgern nicht so leicht manipuliert werden können und die Macht der Nachrichtenmedien begrenzter ist als oft angenommen. Stattdessen finden Framing-Effekte unter sehr spezifischen Bedingungen statt, die häufig nicht erfüllt sind. Das sich abzeichnende Bild der öffentlichen Meinung zeichnet sich durch kristallisierte Einstellungen aus, die ausschliesslich auf neuartige Ereignisse reagieren. Aus dieser Sicht ist Politik ein Muster aufeinander folgender kritischer Ereignisse, von denen jedes eine einzigartige Gelegenheit bietet, das vorherrschende Verständnis eines Themas zu ändern.A large experimental literature on framing effects suggests that citizens form rather limited political preferences, open to severe manipulation. If citizens’ attitudes were always so easily malleable for media outlets and political actors, it would not constitute a very meaningful input for the democratic process. This dissertation asks how these experimental findings translate into complex, realworld news environments and whether news frames structure citizens’ voting intentions. It provides a clear conceptualization of frames, on which it builds a method to identify news frames automatically, and theorises a link between news frames and voting intentions. The dissertation presents original and secondary data, exploring the relationship of news framing, immigration attitudes, and voting intentions. Providing a broad overview of immigration framing in the German news media, it shows that neither immigration attention nor framing can explain the rise of the radical-right AfD. It then exploits a change in the immigration framing of Germany’s largest tabloid, Bild, showing that this shift had no effects on immigration attitudes or voting intentions among its readers. The final empirical chapter presents experimental evidence revealing that framing only affects voting intentions among rather uninformed citizens. The findings contribute to the study of framing and public opinion, suggesting that citizens’ attitudes are not as easily manipulated and the power of the news media more limited than often thought. Instead, framing effects take place under highly specific conditions, which are often not fulfilled. The emerging picture of public opinion is one of crystallized and resistant attitudes, which only respond to novel events. In other words: whoever gets to the voter first, wins. Politics, in this view, is a pattern of critical events following upon each other, each presenting a unique opportunity to change the dominant understanding of an issue

Artificial Intelligence Technology

This open access book aims to give our readers a basic outline of today’s research and technology developments on artificial intelligence (AI), help them to have a general understanding of this trend, and familiarize them with the current research hotspots, as well as part of the fundamental and common theories and methodologies that are widely accepted in AI research and application. This book is written in comprehensible and plain language, featuring clearly explained theories and concepts and extensive analysis and examples. Some of the traditional findings are skipped in narration on the premise of a relatively comprehensive introduction to the evolution of artificial intelligence technology. The book provides a detailed elaboration of the basic concepts of AI, machine learning, as well as other relevant topics, including deep learning, deep learning framework, Huawei MindSpore AI development framework, Huawei Atlas computing platform, Huawei AI open platform for smart terminals, and Huawei CLOUD Enterprise Intelligence application platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence

Review on Photomicrography based Full Blood Count (FBC) Testing and Recent Advancements

With advancements in related sub-fields, research on photomicrography in life science is emerging and this is a review on its application towards human full blood count testing which is a primary test in medical practices. For a prolonged period of time, analysis of blood samples is the basis for bio medical observations of living creatures. Cell size, shape, constituents, count, ratios are few of the features identified using DIP based analysis and these features provide an overview of the state of human body which is important in identifying present medical conditions and indicating possible future complications. In addition, functionality of the immune system is observed using results of blood tests. In FBC tests, identification of different blood cell types and counting the number of cells of each type is required to obtain results. Literature discuss various techniques and methods and this article presents an insightful review on human blood cell morphology, photomicrography, digital image processing of photomicrographs, feature extraction and classification, and recent advances. Integration of emerging technologies such as microfluidics, micro-electromechanical systems, and artificial intelligence based image processing algorithms and classifiers with cell sensing have enabled exploration of novel research directions in blood testing applications.

Using metrics from multiple layers to detect attacks in wireless networks

The IEEE 802.11 networks are vulnerable to numerous wireless-specific attacks. Attackers can implement MAC address spoofing techniques to launch these attacks, while masquerading themselves behind a false MAC address. The implementation of Intrusion Detection Systems has become fundamental in the development of security infrastructures for wireless networks. This thesis proposes the designing a novel security system that makes use of metrics from multiple layers of observation to produce a collective decision on whether an attack is taking place. The Dempster-Shafer Theory of Evidence is the data fusion technique used to combine the evidences from the different layers. A novel, unsupervised and self- adaptive Basic Probability Assignment (BPA) approach able to automatically adapt its beliefs assignment to the current characteristics of the wireless network is proposed. This BPA approach is composed of three different and independent statistical techniques, which are capable to identify the presence of attacks in real time. Despite the lightweight processing requirements, the proposed security system produces outstanding detection results, generating high intrusion detection accuracy and very low number of false alarms. A thorough description of the generated results, for all the considered datasets is presented in this thesis. The effectiveness of the proposed system is evaluated using different types of injection attacks. Regarding one of these attacks, to the best of the author knowledge, the security system presented in this thesis is the first one able to efficiently identify the Airpwn attack

Dystopian Trademark Revelations

Uncovering dystopian technologies is challenging. Nondisclosure agreements, procurement policies, trade secrets, and strategic obfuscation collude to shield the development and deployment of these technologies from public scrutiny until it is too late to combat them with law or policy. But occasionally, exposing dystopian technologies is simple. Corporations choose technology trademarks inspired by dystopian philosophies and novels or similar elements of real life—all warnings that their potential uses are dystopian as well. That pronouncement is not necessarily trumpeted on social media or corporate websites, however. It is revealed in a more surprising place: trademark registrations at the U.S. Patent and Trademark Office (USPTO). To grant registrations, the USPTO demands detailed disclosures about applied-for trademarks. These include the mark itself as well as information about how the applicant will use the mark, forcing corporations to admit their intent for their technologies. But these details do not always provide the full picture. The public can strategically supplement trademark disclosures with knowledge of the dystopian inspiration for the marks to understand corporations’ plans for their products. This Essay uses the marks PALANTIR for big data analytics, PANOPTO for classroom recording systems, and MECHANICAL TURK for on-demand work to illustrate the power of coupling trademark registrations with underlying namesakes to understand technologies’ dystopian implementations. Dystopian trademarks signal dystopian technologies, and the public is well-positioned to seek them out and develop strategies to combat their entrenchment

Artificial Intelligence Technology

This open access book aims to give our readers a basic outline of today’s research and technology developments on artificial intelligence (AI), help them to have a general understanding of this trend, and familiarize them with the current research hotspots, as well as part of the fundamental and common theories and methodologies that are widely accepted in AI research and application. This book is written in comprehensible and plain language, featuring clearly explained theories and concepts and extensive analysis and examples. Some of the traditional findings are skipped in narration on the premise of a relatively comprehensive introduction to the evolution of artificial intelligence technology. The book provides a detailed elaboration of the basic concepts of AI, machine learning, as well as other relevant topics, including deep learning, deep learning framework, Huawei MindSpore AI development framework, Huawei Atlas computing platform, Huawei AI open platform for smart terminals, and Huawei CLOUD Enterprise Intelligence application platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence