Free Trial Signup - Gather Twitter Data DiscoverText

#artificialintelligence

Use this information to train machine-learning classifiers to recognize relevant text and social media data. Jump into data using an interactive word CloudExplorer or build a mini topic dictionary using "defined" search.


Understanding Digital Networks: An Overview of DiscoverText and NodeXL

@machinelearnbot

Social media platforms have now become established as important communication tools, and are used by a subset of the global population. These platforms were originally intended for people to use them for personal use. Overtime, social media platforms were used to gain commercial insight, and for academic research. For brands, social media are fruitful platforms for gaining historical and real-time insights into consumer views. Moreover, these tools can be used for brand and reputation management, as well as crisis detection.


ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems

arXiv.org Artificial Intelligence

Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The learning machine leverages big data to find examples that maximize the training value of its interaction with the teacher. When the teacher is restricted to labeling examples selected by the machine, this problem is an instance of active learning. When the teacher can provide additional information to the machine (e.g., suggestions on what examples or predictive features should be used) as the learning task progresses, then the problem becomes one of interactive learning. To accommodate the two-way communication channel needed for efficient interactive learning, the teacher and the machine need an environment that supports an interaction language. The machine can access, process, and summarize more examples than the teacher can see in a lifetime. Based on the machine's output, the teacher can revise the definition of the task or make it more precise. Both the teacher and the machine continuously learn and benefit from the interaction. We have built a platform to (1) produce valuable and deployable models and (2) support research on both the machine learning and user interface challenges of the interactive learning problem. The platform relies on a dedicated, low-latency, distributed, in-memory architecture that allows us to construct web-scale learning machines with quick interaction speed. The purpose of this paper is to describe this architecture and demonstrate how it supports our research efforts. Preliminary results are presented as illustrations of the architecture but are not the primary focus of the paper.



Interactive Semantic Featuring for Text Classification

arXiv.org Machine Learning

In text classification, dictionaries can be used to define human-comprehensible features. We propose an improvement to dictionary features called smoothed dictionary features. These features recognize document contexts instead of n-grams. We describe a principled methodology to solicit dictionary features from a teacher, and present results showing that models built using these human-comprehensible features are competitive with models trained with Bag of Words features.