Sentiment Analysis with Incremental Human-in-the-Loop Learning and Lexical Resource Customization

The adjustment of probabilistic models for sentiment analysis to changes in language use and the perception of products can be realized via incremental learning techniques. We provide a free, open and GUI-based sentiment analysis tool that allows for a) relabeling predictions and/or adding labeled instances to retrain the weights of a given model, and b) customizing lexical resources to account for false positives and false negatives in sentiment lexicons. Our results show that incrementally updating a model with information from new and labeled instances can substantially increase accuracy. The provided solution can be particularly helpful for gradually refining or enhancing models in an easily accessible fashion while avoiding a) the costs for training a new model from scratch and b) the deterioration of prediction accuracy over time.Anheuser-Busch InBevOpe

SCTG: Social Communications Temporal Graph – A novel approach to visualize temporal communication graphs from social data

Communication on social channels such as social media websites, email, forums, and groups; follows an inherent temporal network structure. Herein, each communication e.g. a post, occurs at a specific point in time, which can be extracted from its metadata. Furthermore, each communication is also linked to a creator e.g. a user, organization, topic, or another communication—which created the communication. Finally, the communication items can be tagged with additional numeric metadata which can be used to score some attributes about the communication e.g. number of comments, retweets, or shares. SCTG is a web based visualization which builds on the visualization principles of –overview, zoom, filter, details-on-demand, relate, history, and extracts (Shneiderman 1996). An example of our visualization applied to a course Facebook group is shown in Figure 1. The tool allows users to highlight a timeline slice, highlight communications matching a specific creator, and identify the temporal connection between each communication and its creator. The visualization tool has also been successfully applied to visualize sentiment of tweets and it’s users as part of the SAIL project (Mishra et al. 2015). The visualization tool is aimed at highlighting the temporal communication nature of social communication channels and is made available as an open source javascript API which can be used by developers and researchers alike.Ope

Skilling up for CRM: qualifications for CRM professionals in the Fourth Industrial Revolution

The 4th industrial revolution (4IR) describes a series of innovations in artificial intelligence, ubiquitous internet connectivity, and robotics, along with the subsequent disruption to the means of production. The impact of 4IR on industry reveals a construct called Industry 4.0. Higher education, too, is called to transform to respond to the disruption of 4IR, to meet the needs of industry, and to maximize human flourishing. Education 4.0 describes 4IR’s impact or predicted impact or intended impact on higher education, including prescriptions for HE’s transformation to realize these challenges. Industry 4.0 requires a highly skilled workforce, and a 4IR world raises questions about skills portability, durability, and lifespan. Every vertical within industry will be impacted by 4IR and such impact will manifest in needs for diverse employees possessing distinct competencies. Customer relationship management (CRM) describes the use of information systems to implement a customer-centric strategy and to practice relationship marketing (RM). Salesforce, a market leading CRM vendor, proposes its products alone will generate 9 million new jobs and $1.6 trillion in new revenues for Salesforce customers by 2024. Despite the strong market for CRM skills, a recent paper in a prominent IS journal claims higher education is not preparing students for CRM careers. In order to supply the CRM domain with skilled workers, it is imperative that higher education develop curricula oriented toward the CRM professional. Assessing skills needed for specific industry roles has long been an important task in IS pedagogy, but we did not find a paper in our literature review that explored the Salesforce administrator role. In this paper, we report the background, methodology, and results of a content analysis of Salesforce Administrator job postings retrieved from popular job sites. We further report the results of semi-structured interviews with industry experts, which served to validate, revise, and extend the content analysis framework. Our resulting skills framework serves as a foundation for CRM curriculum development and our resulting analysis incorporates elements of Education 4.0 to provide a roadmap for educating students to be successful with CRM in a 4IR world

Report on the 2015 NSF Workshop on Unified Annotation Tooling

On March 30 & 31, 2015, an international group of twenty-three researchers with expertise in linguistic annotation convened in Sunny Isles Beach, Florida to discuss problems with and potential solutions for the state of linguistic annotation tooling. The participants comprised 14 researchers from the U.S. and 9 from outside the U.S., with 7 countries and 4 continents represented, and hailed from fields and specialties including computational linguistics, artificial intelligence, speech processing, multi-modal data processing, clinical & medical natural language processing, linguistics, documentary linguistics, sign-language linguistics, corpus linguistics, and the digital humanities. The motivating problem of the workshop was the balkanization of annotation tooling, namely, that even though linguistic annotation requires sophisticated tool support to efficiently generate high-quality data, the landscape of tools for the field is fractured, incompatible, inconsistent, and lacks key capabilities. The overall goal of the workshop was to chart the way forward, centering on five key questions: (1) What are the problems with current tool landscape? (2) What are the possible benefits of solving some or all of these problems? (3) What capabilities are most needed? (4) How should we go about implementing these capabilities? And, (5) How should we ensure longevity and sustainability of the solution? I surveyed the participants before their arrival, which provided significant raw material for ideas, and the workshop discussion itself resulted in identification of ten specific classes of problems, five sets of most-needed capabilities. Importantly, we identified annotation project managers in computational linguistics as the key recipients and users of any solution, thereby succinctly addressing questions about the scope and audience of potential solutions. We discussed management and sustainability of potential solutions at length. The participants agreed on sixteen recommendations for future work. This technical report contains a detailed discussion of all these topics, a point-by-point review of the discussion in the workshop as it unfolded, detailed information on the participants and their expertise, and the summarized data from the surveys

Service Abstractions for Scalable Deep Learning Inference at the Edge

Deep learning driven intelligent edge has already become a reality, where millions of mobile, wearable, and IoT devices analyze real-time data and transform those into actionable insights on-device. Typical approaches for optimizing deep learning inference mostly focus on accelerating the execution of individual inference tasks, without considering the contextual correlation unique to edge environments and the statistical nature of learning-based computation. Specifically, they treat inference workloads as individual black boxes and apply canonical system optimization techniques, developed over the last few decades, to handle them as yet another type of computation-intensive applications. As a result, deep learning inference on edge devices still face the ever increasing challenges of customization to edge device heterogeneity, fuzzy computation redundancy between inference tasks, and end-to-end deployment at scale. In this thesis, we propose the first framework that automates and scales the end-to-end process of deploying efficient deep learning inference from the cloud to heterogeneous edge devices. The framework consists of a series of service abstractions that handle DNN model tailoring, model indexing and query, and computation reuse for runtime inference respectively. Together, these services bridge the gap between deep learning training and inference, eliminate computation redundancy during inference execution, and further lower the barrier for deep learning algorithm and system co-optimization. To build efficient and scalable services, we take a unique algorithmic approach of harnessing the semantic correlation between the learning-based computation. Rather than viewing individual tasks as isolated black boxes, we optimize them collectively in a white box approach, proposing primitives to formulate the semantics of the deep learning workloads, algorithms to assess their hidden correlation (in terms of the input data, the neural network models, and the deployment trials) and merge common processing steps to minimize redundancy

Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding