We present Twitmo, a package that provides a broad range of methods to collect, pre-process, analyze and visualize geo-tagged Twitter data. Twitmo enables the user to collect geo-tagged Tweets from Twitter and and provides a comprehensive and user-friendly toolbox to generate topic distributions from Latent Dirichlet Allocations (LDA), correlated topic models (CTM) and structural topic models (STM). Functions are included for pre-processing of text, model building and prediction. In addition, one of the innovations of the package is the automatic pooling of Tweets into longer pseudo-documents using hashtags and cosine similarities for better topic coherence. The package additionally comes with functionality to visualize collected data sets and fitted models in static as well as interactive ways and offers built-in support for model visualizations via LDAvis providing great convenience for researchers in this area. The Twitmo package is an innovative toolbox that can be used to analyze public discourse of various topics, political parties or persons of interest in space and time.

Understanding key insights from full-text scholarly articles is essential as it enables us to determine interesting trends, give insight into the research and development, and build knowledge graphs. However, some of the interesting key insights are only available when considering full-text. Although researchers have made significant progress in information extraction from short documents, extraction of scientific entities from full-text scholarly literature remains a challenging problem. This work presents an automated End-to-end Research Entity Extractor called EneRex to extract technical facets such as dataset usage, objective task, method from full-text scholarly research articles. Additionally, we extracted three novel facets, e.g., links to source code, computing resources, programming language/libraries from full-text articles. We demonstrate how EneRex is able to extract key insights and trends from a large-scale dataset in the domain of computer science. We further test our pipeline on multiple datasets and found that the EneRex improves upon a state of the art model. We highlight how the existing datasets are limited in their capacity and how EneRex may fit into an existing knowledge graph. We also present a detailed discussion with pointers for future research. Our code and data are publicly available at https://github.com/DiscoveryAnalyticsCenter/EneRex.

Student Evaluations of Teaching (SETs) are widely used in colleges and universities. Typically SET results are summarized for instructors in a static PDF report. The report often includes summary statistics for quantitative ratings and an unsorted list of open-ended student comments. The lack of organization and summarization of the raw comments hinders those interpreting the reports from fully utilizing informative feedback, making accurate inferences, and designing appropriate instructional improvements. In this work, we introduce a novel system, SETSum, that leverages sentiment analysis, aspect extraction, summarization, and visualization techniques to provide organized illustrations of SET findings to instructors and other reviewers. Ten university professors from diverse departments serve as evaluators of the system and all agree that SETSum helps them interpret SET results more efficiently; and 6 out of 10 instructors prefer our system over the standard static PDF report (while the remaining 4 would like to have both). This demonstrates that our work holds the potential to reform the SET reporting conventions in the future. Our code is available at https://github.com/evahuyn/SETSum

The leading company in artificial intelligence (AI) for language understanding expert.ai In their 26-criterion evaluation of document-oriented text analytics platforms, Forrester identified the 12 most significant platforms in the market; their 29-criterion evaluation of people-focused text analytics identified the 13 most significant platforms. "When enterprises want to put NLP solutions into production--not just as experiments, but real solutions--they need to go beyond traditional tools, single techniques and over-hyped language models," said Luca Scagliarini, expert.ai "We believe this recognition by Forrester shows that the market is beginning to align with our vision, which is based on providing the most effective mix of natural language tools and techniques to address all of your strategic business use cases within a single, purpose-built NLP platform." According to both Forrester reports, "Expert.ai is a great choice for customers looking to build customized text analytics applications using hybrid AI (the combination of knowledge/symbolic and machine learning techniques), or customers looking to leverage knowledge-based AI for out of the box accuracy, model explainability (a key in highly regulated industries), or who are not ready to invest in the full ModelOps cycle."

Elon Musk has gained access to the Twitter data that he said was needed to complete his $44 billion acquisition, but data scientists and specialists doubt the stream will provide the conclusive answers he seeks about the number of phony accounts on the platform. After some legal back-and-forth between the two sides, Twitter in recent weeks provided Mr. Musk with historical tweet data and access to its so-called fire hose of tweets, people familiar with the matter said. That fire hose shows the full flood of all tweets--people post hundreds of millions of times a day on the platform, according to the company--in near real time.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. "When captured electronically, customer sentiment -- expressions beyond facts, that convey mood, opinion, and emotion -- carries immense… It's free, we don't spam, and we never share your email address.

Sentiment Analysis is a vast and promising field in data analytics and trading. It is a rapidly rising type of analysis that uses the current pulse and market feeling to detect what participants intend to do or what positions they are holding.

… expressed in a text by a person could be understood by machine learning algorithms. … Let’s deep dive into these three negation types.

The man in this video is over 100 years old! John Hamilton is my inspiration. It is never too late to take care of your health. He exercises at the gym five days a week. This had me thinking: What keeps his body moving without pain?

If the question'What is sentiment analysis?' popped up in your mind as you clicked on this blog, I think you will find my first blog in this series interesting. Essentially, sentiment analysis is a natural language processing technique used to determine the emotional tone of textual data. It is primarily used to understand customer satisfaction, and gauge brand reputation, call center interactions as well as customer feedback and messages. There are various types of sentiment analysis that are common in the real world. In this part of my blog series, let me walk you through the implementation of sentiment analysis.