In this study, we present an approach and a dataset for aspect-based sentiment analysis, showing how we extract and classify aspect phrases. The research field of aspect-based sentiment analysis aims at finding opinions expressed for individual characteristics of products or services in natural language texts. In the literature, reviews for common products or services such as smartphones or restaurants were mostly investigated. We describe our newly annotated dataset of German physician reviews, which presents a sensitive and linguistically complex domain, taking care to describe the annotation process and the functionality of our neural network approach. Finally, we introduce a model that can extract and classify aspect phrases in one step while obtaining an F1 score of 80%. As we employ our algorithm in a more complex domain, we believe that our study outperforms other studies.

Time is deeply woven into how people perceive, and communicate about the world. Almost unconsciously, we provide our language utterances with temporal cues, like verb tenses, and we can hardly produce sentences without such cues. Extracting temporal cues from text, and constructing a global temporal view about the order of described events is a major challenge of automatic natural language understanding. Temporal reasoning, the process of combining different temporal cues into a coherent temporal view, plays a central role in temporal information extraction. This article presents a comprehensive survey of the research from the past decades on temporal reasoning for automatic temporal information extraction from text, providing a case study on the integration of symbolic reasoning with machine learning-based information extraction systems.

To build Sounding Board, we develop a system architecture that is capable of accommodating dialog strategies that we designed for socialbot conversations. The architecture consists of a multi-dimensional language understanding module for analyzing user utterances, a hierarchical dialog management framework for dialog context tracking and complex dialog control, and a language generation process that realizes the response plan and makes adjustments for speech synthesis. Additionally, we construct a new knowledge base to power the socialbot by collecting social chat content from a variety of sources. An important contribution of the system is the synergy between the knowledge base and the dialog management, i.e., the use of a graph structure to organize the knowledge base that makes dialog control very efficient in bringing related content to the discussion. Using the data collected from Sounding Board during the competition, we carry out in-depth analyses of socialbot conversations and user ratings which provide valuable insights in evaluation methods for socialbots. We additionally investigate a new approach for system evaluation and diagnosis that allows scoring individual dialog segments in the conversation. Finally, observing that socialbots suffer from the issue of shallow conversations about topics associated with unstructured data, we study the problem of enabling extended socialbot conversations grounded on a document. To bring together machine reading and dialog control techniques, a graph-based document representation is proposed, together with methods for automatically constructing the graph. Using the graph-based representation, dialog control can be carried out by retrieving nodes or moving along edges in the graph. To illustrate the usage, a mixed-initiative dialog strategy is designed for socialbot conversations on news articles.

Udemy Course Text Mining and Sentiment Analysis with Tableau and R NED Text Analysis 101: Sentiment Analysis in Tableau & R. At the Tableau Partner Summit in London I attended a session about statistics and sets in Tableau. In this session, Oliver Linder, Sales Consultant at Tableau Bestseller What you'll learn Connect Twitter and R to harvest Tweets for certain keywords Perform sentiment analysis based on a simple lexicon approach Clean and process Tweets for further analysis Export text based data and sentiment scores from R Use Tableau to visualize sentiment analysis data Identify situations where sentiment analysis can be applied in a company Description Extract valuable info out of Twitter for marketing, finance, academic or professional research and much more. This course harnesses the upside of R and Tableau to do sentiment analysis on Twitter data. With sentiment analysis you find out if the crowd has a rather positive or negative opinion towards a given search term. This search term can be a product (like in the course) but it can also be a person, region, company or basically anything as long as it is mentioned regularly on Twitter.

Building equitable and inclusive NLP technologies demands consideration of whether and how social attitudes are represented in ML models. In particular, representations encoded in models often inadvertently perpetuate undesirable social biases from the data on which they are trained. In this paper, we present evidence of such undesirable biases towards mentions of disability in two different English language models: toxicity prediction and sentiment analysis. Next, we demonstrate that the neural embeddings that are the critical first step in most NLP pipelines similarly contain undesirable biases towards mentions of disability. We end by highlighting topical biases in the discourse about disability which may contribute to the observed model biases; for instance, gun violence, homelessness, and drug addiction are over-represented in texts discussing mental illness.

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses. Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain.

But how do you turn that feedback into meaningful customer insights? In the past, companies used things like surveys to try to narrow down a general good/bad/neutral response to their recent marketing campaign or product. Still, there is so much more information in the form of unstructured data that could help companies better understand their customers. Whether they are using social media, blogs, forums, reviews, or online news commenting, customers are sharing their opinions in tons of different ways every single day. The only issue: many of these opinions are shared in nuanced ways that traditional AI hasn't been able to navigate.

Video conference app Zoom illegally shared personal data with Facebook, even if users did not have a Facebook account, a lawsuit claims. The app has experienced a surge in popularity as millions of people around the world are forced to work from home as part of coronavirus containment measures. The lawsuit, which was filed in a California federal court on Monday, states that the company failed to inform users that their data was being sent to Facebook "and possibly other third parties". It states: "Had Zoom informed its users that it would use inadequate security measures and permit unauthorised third-party tracking of their personal information, users... would not have been willing to use the Zoom App." The allegations come amid a flurry of questions surrounding Zoom's privacy policies, with the Electronic Frontier Foundation recently warning that the app allows administrators to track the activities of attendees.

Narratives are an important human tool for communication, representation and understanding. Natural Language Processing already offers many instruments that enable the automatic extraction of narrative elements from texts, including Named Entity Recognition, Semantic Role Labeling, Sentiment Analysis, Anaphora Resolution, Temporal Reasoning, etc. The storyfication of data is being used to generate textual reports on finance and sports, among others. Timelines and infographics can be employed to represent in a more compact way automatically identified narrative chains in a large set of news articles, assisting human readers in grasping complex stories with different moments and a network of characters. While the Automatic Generation of Text shows impressive results towards computational creativity, it still needs to develop means for controlling the narrative intent of the output.

In the Finance Industry, Alternative Data is used to give investors an information advantage. Quantitative Hedge Funds have used trading models based on Alternative Data for many years. The most common Alternative Data signal used in quantitative trading and quantitative investing is based on text data from the Internet, and the trading models can broadly be defined as algorithmic trading models and as statistical arbitrage models. It has been suggested that text analysis is the key to success for the most successful money manager of all times. The trading model can use text data and sentiment data as the only, or as one of several, inputs, and it can be the main strategy, or one of several strategies, in a hedge fund. Some traditional funds use text-based signals to build the models they use as an overlay to other strategies and as a risk indicator for tactical asset allocation.