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Adapting Bert For Target-Oriented Multimodal Sentiment Classification, Jianfei Yu, Jing Jiang
Adapting Bert For Target-Oriented Multimodal Sentiment Classification, Jianfei Yu, Jing Jiang
Research Collection School Of Computing and Information Systems
As an important task in Sentiment Analysis, Target-oriented Sentiment Classification (TSC) aims to identify sentiment polarities over each opinion target in a sentence. However, existing approaches to this task primarily rely on the textual content, but ignoring the other increasingly popular multimodal data sources (e.g., images), which can enhance the robustness of these text-based models. Motivated by this observation and inspired by the recently proposed BERT architecture, we study Target-oriented Multimodal Sentiment Classification (TMSC) and propose a multimodal BERT architecture. To model intra-modality dynamics, we first apply BERT to obtain target-sensitive textual representations. We then borrow the idea from self-attention …
Biker: A Tool For Bi-Information Source Based Api Method Recommendation, Liang Cai, Haoye Wang, Qiao Huang, Xin Xia, Zhenchang Xing, David Lo
Biker: A Tool For Bi-Information Source Based Api Method Recommendation, Liang Cai, Haoye Wang, Qiao Huang, Xin Xia, Zhenchang Xing, David Lo
Research Collection School Of Computing and Information Systems
No abstract provided.
Multi-Channel Graph Neural Network For Entity Alignment, Yixin Cao, Zhiyuan Liu, Chengjiang Li, Zhiyuan Liu, Juanzi Li, Tat-Seng Chua
Multi-Channel Graph Neural Network For Entity Alignment, Yixin Cao, Zhiyuan Liu, Chengjiang Li, Zhiyuan Liu, Juanzi Li, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented knowledge graph (KG) embeddings by robustly encoding two KGs via multiple channels. Each channel encodes KGs via different relation weighting schemes with respect to self-attention towards KG completion and cross-KG attention for pruning exclusive entities respectively, which are further combined via pooling techniques. Moreover, we also infer and transfer rule knowledge for completing two KGs consistently. MuGNN is expected to reconcile the structural differences of two KGs, and thus make …
Modeling Intra-Relation In Math Word Problems With Different Functional Multi-Head Attentions, Jierui Li, Lei Wang, Jipeng Zhang, Yan Wang, Bing Tian Dai, Dongxiang Zhang
Modeling Intra-Relation In Math Word Problems With Different Functional Multi-Head Attentions, Jierui Li, Lei Wang, Jipeng Zhang, Yan Wang, Bing Tian Dai, Dongxiang Zhang
Research Collection School Of Computing and Information Systems
Several deep learning models have been proposed for solving math word problems (MWPs) automatically. Although these models have the ability to capture features without manual efforts, their approaches to capturing features are not specifically designed for MWPs. To utilize the merits of deep learning models with simultaneous consideration of MWPs’ specific features, we propose a group attention mechanism to extract global features, quantity-related features, quantity-pair features and question-related features in MWPs respectively. The experimental results show that the proposed approach performs significantly better than previous state-of-the-art methods, and boost performance from 66.9% to 69.5% on Math23K with training-test split, from …
Unsupervised Deep Structured Semantic Models For Commonsense Reasoning, Shuohang Wang, Sheng Zhang, Yelong Shen, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Jing Jiang
Unsupervised Deep Structured Semantic Models For Commonsense Reasoning, Shuohang Wang, Sheng Zhang, Yelong Shen, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Jing Jiang
Research Collection School Of Computing and Information Systems
Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches.
Political Discussions In Homogeneous And Cross-Cutting Communication Spaces, Qatar Computing Research Institute, Haewoon Kwak, Universität Bamberg
Political Discussions In Homogeneous And Cross-Cutting Communication Spaces, Qatar Computing Research Institute, Haewoon Kwak, Universität Bamberg
Research Collection School Of Computing and Information Systems
Online platforms, such as Facebook, Twitter, and Reddit, provide users with a rich set of features for sharing and consuming political information, expressing political opinions, and exchanging potentially contrary political views. In such activities, two types of communication spaces naturally emerge: those dominated by exchanges between politically homogeneous users and those that allow and encourage crosscutting exchanges in politically heterogeneous groups. While research on political talk in online environments abounds, we know surprisingly little about the potentially varying nature of discussions in politically homogeneous spaces as compared to cross-cutting communication spaces. To fill this gap, we use Reddit to explore …
Learning Cross-Modal Embeddings With Adversarial Networks For Cooking Recipes And Food Images, Hao Wang, Doyen Sahoo, Chenghao Liu, Ee-Peng Lim, Steven C. H. Hoi
Learning Cross-Modal Embeddings With Adversarial Networks For Cooking Recipes And Food Images, Hao Wang, Doyen Sahoo, Chenghao Liu, Ee-Peng Lim, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Food computing is playing an increasingly important role in human daily life, and has found tremendous applications in guiding human behavior towards smart food consumption and healthy lifestyle. An important task under the food-computing umbrella is retrieval, which is particularly helpful for health related applications, where we are interested in retrieving important information about food (e.g., ingredients, nutrition, etc.). In this paper, we investigate an open research task of cross-modal retrieval between cooking recipes and food images, and propose a novel framework Adversarial Cross-Modal Embedding (ACME) to resolve the cross-modal retrieval task in food domains. Specifically, the goal is to …
Dynamic Fusion With Intra-And Inter-Modality Attention Flow For Visual Question Answering, Peng Gao, Zhengkai Jiang, Haoxuan You, Pan Lu, Steven C. H. Hoi, Xiaogang Wang, Hongsheng Li
Dynamic Fusion With Intra-And Inter-Modality Attention Flow For Visual Question Answering, Peng Gao, Zhengkai Jiang, Haoxuan You, Pan Lu, Steven C. H. Hoi, Xiaogang Wang, Hongsheng Li
Research Collection School Of Computing and Information Systems
Learning effective fusion of multi-modality features is at the heart of visual question answering. We propose a novel method of dynamically fusing multi-modal features with intra- and inter-modality information flow, which alternatively pass dynamic information between and across the visual and language modalities. It can robustly capture the high-level interactions between language and vision domains, thus significantly improves the performance of visual question answering. We also show that the proposed dynamic intra-modality attention flow conditioned on the other modality can dynamically modulate the intramodality attention of the target modality, which is vital for multimodality feature fusion. Experimental evaluations on the …
Importance Sampling Of Interval Markov Chains, Cyrille Jegourel, Jingyi Wang, Jun Sun
Importance Sampling Of Interval Markov Chains, Cyrille Jegourel, Jingyi Wang, Jun Sun
Research Collection School Of Computing and Information Systems
In real-world systems, rare events often characterize critical situations like the probability that a system fails within some time bound and they are used to model some potentially harmful scenarios in dependability of safety-critical systems. Probabilistic Model Checking has been used to verify dependability properties in various types of systems but is limited by the state space explosion problem. An alternative is the recourse to Statistical Model Checking (SMC) that relies on Monte Carlo simulations and provides estimates within predefined error and confidence bounds. However, rare properties require a large number of simulations before occurring at least once. To tackle …
Concluding Remarks, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch
Concluding Remarks, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch
Research Collection School Of Computing and Information Systems
This chapter summarizes the major contributions in this book and discusses their possible positions and requirements in some future scenarios. Section 8.1 follows the book structure to revisit the key contributions of this book in both theories and applications. The developed algorithms, such as the VA-ARTs for hyperparameter adaptation and the GHF-ART for multimedia representation and fusion, and the four applications, such as clustering and retrieving socially enriched multimedia data, are concentrated using one paragraph and three paragraphs, respectively. In Sect. 8.2, the roles of the proposed ART-embodied algorithms in social media clustering tasks are highlighted, and their possible evolutions …
Neural Multimodal Belief Tracker With Adaptive Attention For Dialogue Systems, Zheng Zhang, Lizi Liao, Minlie Huang, Xiaoyan Zhu, Tat-Seng Chua
Neural Multimodal Belief Tracker With Adaptive Attention For Dialogue Systems, Zheng Zhang, Lizi Liao, Minlie Huang, Xiaoyan Zhu, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Multimodal dialogue systems are attracting increasing attention with a more natural and informative way for human-computer interaction. As one of its core components, the belief tracker estimates the user's goal at each step of the dialogue and provides a direct way to validate the ability of dialogue understanding. However, existing studies on belief trackers are largely limited to textual modality, which cannot be easily extended to capture the rich semantics in multimodal systems such as those with product images. For example, in fashion domain, the visual appearance of clothes play a crucial role in understanding the user's intention. In this …
Emerging App Issue Identification From User Feedback: Experience On Wechat, Cuiyun Gao, Wujie Zheng, Yuetang Deng, David Lo, Jichuan Zeng, Michael R. Lyu, Irwin King
Emerging App Issue Identification From User Feedback: Experience On Wechat, Cuiyun Gao, Wujie Zheng, Yuetang Deng, David Lo, Jichuan Zeng, Michael R. Lyu, Irwin King
Research Collection School Of Computing and Information Systems
It is vital for popular mobile apps with large numbers of users to release updates with rich features while keeping stable user experience. Timely and accurately locating emerging app issues can greatly help developers to maintain and update apps. User feedback (i.e., user reviews) is a crucial channel between app developers and users, delivering a stream of information about bugs and features that concern users. Methods to identify emerging issues based on user feedback have been proposed in the literature, however, their applicability in industry has not been explored. We apply the recent method IDEA to WeChat, a popular messenger …
Detect Rumors On Twitter By Promoting Information Campaigns With Generative Adversarial Learning, Jing Ma, Wei Gao, Kam-Fai Wong
Detect Rumors On Twitter By Promoting Information Campaigns With Generative Adversarial Learning, Jing Ma, Wei Gao, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Rumors can cause devastating consequences to individual and/or society. Analysis shows that widespread of rumors typically results from deliberately promoted information campaigns which aim to shape collective opinions on the concerned news events. In this paper, we attempt to fight such chaos with itself to make automatic rumor detection more robust and effective. Our idea is inspired by adversarial learning method originated from Generative Adversarial Networks (GAN). We propose a GAN-style approach, where a generator is designed to produce uncertain or conflicting voices, complicating the original conversational threads in order to pressurize the discriminator to learn stronger rumor indicative representations …
Neural Multimodal Belief Tracker With Adaptive Attention For Dialogue Systems, Zheng Zhang, Lizi Liao, Minlie Huang, Xiaoyan Zhu, Tat-Seng Chua
Neural Multimodal Belief Tracker With Adaptive Attention For Dialogue Systems, Zheng Zhang, Lizi Liao, Minlie Huang, Xiaoyan Zhu, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Multimodal dialogue systems are attracting increasing attention with a more natural and informative way for human-computer interaction. As one of its core components, the belief tracker estimates the user's goal at each step of the dialogue and provides a direct way to validate the ability of dialogue understanding. However, existing studies on belief trackers are largely limited to textual modality, which cannot be easily extended to capture the rich semantics in multimodal systems such as those with product images. For example, in fashion domain, the visual appearance of clothes play a crucial role in understanding the user's intention. In this …
The Challenges Of Creating Engaging Content: Results From A Focus Group Study Of A Popular News Media Organization, Kholoud Khalil Aldous, Jisun An, Bernard J. Jansen
The Challenges Of Creating Engaging Content: Results From A Focus Group Study Of A Popular News Media Organization, Kholoud Khalil Aldous, Jisun An, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
The process of content creation for distribution via social media platforms is not a trivial one for social media editors as the goal of creating both serious and engaging content is challenging, with no clear or differing guidelines or rules across and between platforms. For creators of serious content, such as news organizations, advertisers, or educational institutions, engagement has a deeper meaning beyond likes, shares, etc. that is aimed at the audience actually processing the underlying content associated with a social media post. In this research, we report findings from a group study that aimed to understand the process and …
Multimodal Review Generation For Recommender Systems, Quoc Tuan Truong, Hady W. Lauw
Multimodal Review Generation For Recommender Systems, Quoc Tuan Truong, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Key to recommender systems is learning user preferences, which are expressed through various modalities. In online reviews, for instance, this manifests in numerical rating, textual content, as well as visual images. In this work, we hypothesize that modelling these modalities jointly would result in a more holistic representation of a review towards more accurate recommendations. Therefore, we propose Multimodal Review Generation (MRG), a neural approach that simultaneously models a rating prediction component and a review text generation component. We hypothesize that the shared user and item representations would augment the rating prediction with richer information from review text, while sensitizing …
Deepjit: An End-To-End Deep Learning Framework For Just-In-Time Defect Prediction, Thong Hoang, Hoa Khanh Dam, Yasutaka Kamei, David Lo, Naoyasu Ubayashi
Deepjit: An End-To-End Deep Learning Framework For Just-In-Time Defect Prediction, Thong Hoang, Hoa Khanh Dam, Yasutaka Kamei, David Lo, Naoyasu Ubayashi
Research Collection School Of Computing and Information Systems
Software quality assurance efforts often focus on identifying defective code. To find likely defective code early, change-level defect prediction – aka. Just-In-Time (JIT) defect prediction – has been proposed. JIT defect prediction models identify likely defective changes and they are trained using machine learning techniques with the assumption that historical changes are similar to future ones. Most existing JIT defect prediction approaches make use of manually engineered features. Unlike those approaches, in this paper, we propose an end-to-end deep learning framework, named DeepJIT, that automatically extracts features from commit messages and code changes and use them to identify defects. Experiments …
Automatic Short Answer Grading Using Siamese Bidirectional Lstm Based Regression, Arya Prabhudesai, Nguyen Binh Duong Ta
Automatic Short Answer Grading Using Siamese Bidirectional Lstm Based Regression, Arya Prabhudesai, Nguyen Binh Duong Ta
Research Collection School Of Computing and Information Systems
Automatic student assessment plays an important role in education - it provides instant feedback to learners, and at the same time reduces tedious grading workload for instructors. In this paper, we investigate new machine learning techniques for automatic short answer grading (ASAG). The ASAG problem mainly involves assessing short, natural language responses to given questions automatically. While current research in the field has focused either on feature engineering or deep learning, we propose a new approach which combines the advantages of both. More specifically, we propose a Siamese Bidirectional LSTM Neural Network based Regressor in conjunction with handcrafted features for …
Discursive Power In Contemporary Media Systems: A Comparative Framework, Andreas Jungherr, Oliver Posegga, Jisun An
Discursive Power In Contemporary Media Systems: A Comparative Framework, Andreas Jungherr, Oliver Posegga, Jisun An
Research Collection School Of Computing and Information Systems
Contemporary media systems are in transition. The constellation of organizations, groups, and individuals contributing information to national and international news flows has changed as a result of the digital transformation. The 'hybrid media system' has proven to be one of the most instructive concepts addressing this change. Its focus on the mutually dependent interconnections between various types of media organizations, actors, and publics has inspired prolific research. Yet the concept can tempt researchers to sidestep systematic analyses of information flows and actors’ differing degrees of influence by treating media systems as a black box. To enable large-scale, empirical comparative studies …
Social Media Mining For Journalism, Arkaitz Zubiaga, Bahareh Heravi, Jisun An, Haewoon Kwak
Social Media Mining For Journalism, Arkaitz Zubiaga, Bahareh Heravi, Jisun An, Haewoon Kwak
Research Collection School Of Computing and Information Systems
The exponential growth of social media as a central communication practice, and its agility in capturing and announcing breaking news events more rapidly than traditional media, has changed the journalistic landscape: social media has been adopted as a significant source by professional journalists, and conversely, citizens are able to use social media as a form of direct reportage. This brings along new opportunities for newsrooms and journalists by providing new means for newsgathering through access to a wealth of citizen reportage and updates about current affairs, as well as an additional showcase for news dissemination.
Comparelda: A Topic Model For Document Comparison, Maksim Tkachenko, Hady Wirawan Lauw
Comparelda: A Topic Model For Document Comparison, Maksim Tkachenko, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
A number of real-world applications require comparison of entities based on their textual representations. In this work, we develop a topic model supervised by pairwise comparisons of documents. Such a model seeks to yield topics that help to differentiate entities along some dimension of interest, which may vary from one application to another. While previous supervised topic models consider document labels in an independent and pointwise manner, our proposed Comparative Latent Dirichlet Allocation (CompareLDA) learns predictive topic distributions that comply with the pairwise comparison observations. To fit the model, we derive a maximum likelihood estimation method via augmented variational approximation …
Recommending New Features From Mobile App Descriptions, He Jiang, Jingxuan Zhang, Xiaochen Li, Zhilei Ren, David Lo, Xindong Wu, Zhongxuan Luo
Recommending New Features From Mobile App Descriptions, He Jiang, Jingxuan Zhang, Xiaochen Li, Zhilei Ren, David Lo, Xindong Wu, Zhongxuan Luo
Research Collection School Of Computing and Information Systems
The rapidly evolving mobile applications (apps) have brought great demand for developers to identify new features by inspecting the descriptions of similar apps and acquire missing features for their apps. Unfortunately, due to the huge number of apps, this manual process is time-consuming and unscalable. To help developers identify new features, we propose a new approach named SAFER. In this study, we first develop a tool to automatically extract features from app descriptions. Then, given an app, we leverage the topic model to identify its similar apps based on the extracted features and API names of apps. Finally, we design …
Global Inference For Aspect And Opinion Terms Co-Extraction Based On Multi-Task Neural Networks, Jianfei Yu, Jing Jiang, Rui Xia
Global Inference For Aspect And Opinion Terms Co-Extraction Based On Multi-Task Neural Networks, Jianfei Yu, Jing Jiang, Rui Xia
Research Collection School Of Computing and Information Systems
Extracting aspect terms and opinion terms are two fundamental tasks in opinion mining. The recent success of deep learning has inspired various neural network architectures, which have been shown to achieve highly competitive performance in these two tasks. However, most existing methods fail to explicitly consider the syntactic relations among aspect terms and opinion terms, which may lead to the inconsistencies between the model predictions and the syntactic constraints. To this end, we first apply a multi-task learning framework to implicitly capture the relations between the two tasks, and then propose a global inference method by explicitly modelling several syntactic …
Perflearner: Learning From Bug Reports To Understand And Generate Performance Test Frames, Xue Han, Tingting Yu, David Lo
Perflearner: Learning From Bug Reports To Understand And Generate Performance Test Frames, Xue Han, Tingting Yu, David Lo
Research Collection School Of Computing and Information Systems
Software performance is important for ensuring the quality of software products. Performance bugs, defined as programming errors that cause significant performance degradation, can lead to slow systems and poor user experience. While there has been some research on automated performance testing such as test case generation, the main idea is to select workload values to increase the program execution times. These techniques often assume the initial test cases have the right combination of input parameters and focus on evolving values of certain input parameters. However, such an assumption may not hold for highly configurable real-word applications, in which the combinations …
Making A Good Thing Better: Enhancing Password/Pin-Based User Authentication With Smartwatch, Bing Chang, Yingjiu Li, Qiongxiao Wang, Wen-Tao Zhu, Robert H. Deng
Making A Good Thing Better: Enhancing Password/Pin-Based User Authentication With Smartwatch, Bing Chang, Yingjiu Li, Qiongxiao Wang, Wen-Tao Zhu, Robert H. Deng
Research Collection School Of Computing and Information Systems
Wearing smartwatches becomes increasingly popular in people’s lives. This paper shows that a smartwatch can help its bearer authenticate to a login system effectively and securely even if the bearer’s password has already been revealed. This idea is motivated by our observation that a sensor-rich smartwatch is capable of tracking the wrist motions of its bearer typing a password or PIN, which can be used as an authentication factor. The major challenge in this research is that a sophisticated attacker may imitate a user’s typing behavior as shown in previous research on keystroke dynamics based user authentication. We address this …
Attention-Based Lstm-Cnns For Uncertainty Identification On Chinese Social Media Texts, Binyang Li, Kaiming Zhou, Wei Gao, Xu Han Han, Linna Zhou
Attention-Based Lstm-Cnns For Uncertainty Identification On Chinese Social Media Texts, Binyang Li, Kaiming Zhou, Wei Gao, Xu Han Han, Linna Zhou
Research Collection School Of Computing and Information Systems
Uncertainty identification is an important semantic processing task, which is crucial to the quality of information in terms of factuality in many techniques, e.g. topic detection, question answering. Especially in social media, the texts are written informally which are widely used in many applications, so the factuality has become a premier concern. However, existing approaches that still rely on lexical cues suffer greatly from the casual or word-of-mouth peculiarity of social media, in which the cue phrases are often expressed in sub-standard form or even omitted from sentences. To tackle these problems, this paper proposes the attention-based LSTM-CNNs for the …
An Architectural Design And Evaluation Of An Affective Tutoring System For Novice Programmers, Hua Leong Fwa
An Architectural Design And Evaluation Of An Affective Tutoring System For Novice Programmers, Hua Leong Fwa
Research Collection School Of Computing and Information Systems
Affect is prevalent in learning and it influences students’ learning achievement. This paper details the design and evaluation of an Affective Tutoring System (ATS) that tutors student in computer programming. Although most ATSs are purpose built for a specific domain, making adaptation to another domain difficult, this ATS is architected for adaptability and extensibility. This study also addresses a lack of research exploring the theories and methods of integrating affect and learning within the learning process by proposing methods of regulating the negative affect of students. Both quantitative and qualitative techniques were used for evaluation of the effectiveness of the …
On Learning Psycholinguistics Tools For English-Based Creole Languages Using Social Media Data, Pei-Chi Lo, Ee-Peng Lim
On Learning Psycholinguistics Tools For English-Based Creole Languages Using Social Media Data, Pei-Chi Lo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
The Linguistic Inquiry and Word Count (LIWC) tool is a psycholinguistics tool that has been widely used in both psychology and sociology research, and the LIWC scores derived from user-generated content are known to be good features for personality prediction [1], [2]. LIWC, however, is language specific as it relies on counting the percentage of predefined dictionary words occurring in the content. For content written in English Creoles which are languages based on English, the original English LIWC may not perform optimally due to its lack of words which are only used in the English Creoles. In this paper, we …
Text Analytics Approach To Extract Course Improvement Suggestions From Students’ Feedback, Swapna Gottipati, Venky Shankararaman, Jeff Rongsheng Lin
Text Analytics Approach To Extract Course Improvement Suggestions From Students’ Feedback, Swapna Gottipati, Venky Shankararaman, Jeff Rongsheng Lin
Research Collection School Of Computing and Information Systems
In academic institutions, it is normal practice that at the end of each term, students are required to complete a questionnaire that is designed to gather students’ perceptions of the instructor and their learning experience in the course. Students’ feedback includes numerical answers to Likert scale questions and textual comments to open-ended questions. Within the textual comments given by the students are embedded suggestions. A suggestion can be explicit or implicit. Any suggestion provides useful pointers on how the instructor can further enhance the student learning experience. However, it is tedious to manually go through all the qualitative comments and …
Personalized Microblog Sentiment Classification Via Adversarial Cross-Lingual Learning, Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang
Personalized Microblog Sentiment Classification Via Adversarial Cross-Lingual Learning, Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang
Research Collection School Of Computing and Information Systems
Sentiment expression in microblog posts can be affected by user’s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user’s language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel …