Identifying major tasks and minor tasks within online reviews

© 2017 Elsevier B.V. Many e-commerce websites allow customers to provide reviews that reflect their experiences and opinions about products and services. Such published reviews, whether positive or negative, serve both the consumer and the business. Negative reviews can inform the merchant of issues that, when addressed, may improve the addressed aspect of the business and improve its online reputation. However, when the merchant fails to respond to customers’ concerns, the business faces potential loss of reputation. The Sentiminder system identifies major areas of customer concern, and specific concerns within each area. This helps the merchant to process a large body of reviews and find what needs to be addressed. In this paper we address the problems of quickly finding specific issues and specific comments that are consistently discussed in a negative way. Our technique drills down from the major task areas to more specific issues, assisting the user to accurately determine what issues need attention. The sentiment of reviews on the same topic can vary widely, so we maximize coherence over a variety of six different sentiment assessment techniques. We achieve from about 45% to 65% coherence. These suggestions are implemented in the Sentiminder, an online tool that creates schedules of optimal selections of tasks

Identifying Major Tasks from On-line Reviews

© 2017 The Authors. Published by Elsevier B.V. Many e-commerce websites allow customers to provide reviews that reflect their experiences and opinions about the business\u27s products or services. Such published reviews potentially benefit the business\u27s reputation, improve both current and future customers\u27 trust in the business, and accordingly improve the business. Negative reviews can inform the merchant of issues that, when addressed, also improve the business. However, when reviews reflect negative experiences and the merchant fails to respond, the business faces potential loss of reputation, trust, and damage. We present the Sentiminder system that identifies reviews with negative sentiment, organizes them, and helps the merchant develop a plan with an end date by which issues will be addressed. In this paper we address the problem of quickly finding subtasks in a large set of reviews, which may help the merchant to identify, from the set of reviews, subtasks that need to be addressed. We do this by identify nouns that frequently occur only in the reviews with negative sentiment

Temperature Forecasts with Stable Accuracy in a Smart Home

© 2016 The Authors. We forecast internal temperature in a home with sensors, modeled as a linear function of recent sensor values. When delivering forecasts as a service, two desirable properties are that forecasts have stable accuracy over a variety of forecast horizons - so service levels can be predicted - and that the forecasts rely on a modest amount of sensor history - so forecasting can be restarted soon after any data outage due to, for example, sensor failure. From a publicly available data set, we show that sensor values over the past one or two hours are sufficient to meet these demands. A standard machine learning method based on forward stepwise linear regression with cross validation gives forecasts whose out-of-sample errors increase slowly as the forecast horizon increases, and that are accurate to within one fifth of a degree C over three hours, and to within about one half degree C over six hours, based on one or two hours of history. Previous results from this data achieved errors within one degree C over three hours based on five days of history

Forecasting Temperature in a Smart Home with Segmented Linear Regression

The 9th International Conference on Sustainable Energy Information Technology (SEIT), August 19-21, 2019, Halifax, Nova Scotia, Canad

Opinions Sandbox: Turning Emotions on Topics into Actionable Analytics

© 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. The Opinions Sandbox is a running prototype that accesses comments collected from customers of a particular product or service, and calculates the overall sentiment toward that product or service. It performs topic extraction, displays the comments partitioned into topics, and presents a sentiment for each topic. This helps to quickly digest customers’ opinions, particularly negative ones, and sort them by the concerns expressed by the customers. These topics are now considered issues to be addressed. The Opinions Sandbox does two things with this list of issues. First, it simulates the social network of the future, after rectifying each issue. Comments with positive sentiment regarding this rectified issues are synthesized, they are injected into the comment corpus, and the effect on overall sentiment is produced. Second, it helps the user create a plan for addressing the issues identified in the comments. It uses the quantitative improvement of sentiment, calculated by the simulation in the first part, and it uses user-supplied cost estimates of the effort required to rectify each issue. Sets of possible actions are enumerated and analysed showing both the costs and the benefits. By balancing these benefits against these costs, it recommends actions that optimize the cost/benefit tradeoff

Forecasting Temperature in a Smart Home with Segmented Linear Regression

© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Conference Program Chairs. The efficiency of heating, ventilation and cooling operations in a home are improved when they are controlled by a system that takes into account an accurate forecast of temperature in the house. Temperature forecasts are informed by data from sensors that report on activities and conditions in and around the home. Using publicly available data, we apply linear models based on LASSO regression and our recently developled MIDFEL LASSO regression. These models take into account the past 24 hours of the sensors\u27 data. We have previously identified the most influential sensors in a forecast over the next 48 hours. In this paper, we compute 48 separate one-hour forecast and for each hour we identify the sensors that are most influential. This improves forecast accuracy and reveals which sensors are most valuable to install

Oral once-daily berotralstat for the prevention of hereditary angioedema attacks: A randomized, double-blind, placebo-controlled phase 3 trial

BACKGROUND: Berotralstat (BCX7353) is an oral, once-daily inhibitor of plasma kallikrein in development for the prophylaxis of hereditary angioedema (HAE) attacks. OBJECTIVE: Our aim was to determine the efficacy, safety, and tolerability of berotralstat in patients with HAE over a 24-week treatment period (the phase 3 APeX-2 trial). METHODS: APeX-2 was a double-blind, parallel-group study that randomized patients at 40 sites in 11 countries 1:1:1 to receive once-daily berotralstat in a dose of 110 mg or 150 mg or placebo (Clinicaltrials.gov identifier NCT03485911). Patients aged 12 years or older with HAE due to C1 inhibitor deficiency and at least 2 investigator-confirmed HAE attacks in the first 56 days of a prospective run-in period were eligible. The primary efficacy end point was the rate of investigator-confirmed HAE attacks during the 24-week treatment period. RESULTS: A total of 121 patients were randomized; 120 of them received at least 1 dose of the study drug (n = 41, 40, and 39 in the 110-mg dose of berotralstat, 150-mg of dose berotralstat, and placebo groups, respectively). Berotralstat demonstrated a significant reduction in attack rate at both 110 mg (1.65 attacks per month; P = .024) and 150 mg (1.31 attacks per month; P \u3c .001) relative to placebo (2.35 attacks per month). The most frequent treatment-emergent adverse events that occurred more with berotralstat than with placebo were abdominal pain, vomiting, diarrhea, and back pain. No drug-related serious treatment-emergent adverse events occurred. CONCLUSION: Both the 110-mg and 150-mg doses of berotralstat reduced HAE attack rates compared with placebo and were safe and generally well tolerated. The most favorable benefit-to-risk profile was observed at a dose of 150 mg per day

Selecting Sensors when Forecasting Temperature in Smart Buildings

© 2017 The Authors. Published by Elsevier B.V. Forecasts of temperature in a smart building, i.e. one that is outfitted with sensors, are computed from data gathered by these sensors. Model predictive controllers can use accurate temperature forecasts to save energy by optimally using Heating, Ventilation and Air Conditioners while achieving comfort. We report on experiments from such a house, in which we select different sets of sensors, build a temperature model from each set, and then compare the accuracy of these models. While a primary goal of this research area is to reduce costs by reducing energy consumption, in this paper, besides the cost of energy, we consider the cost of data collection and management. Each sensor employed in the forecast calculation incurs costs for installation and maintenance and an incremental cost for computation. Some sensors, however, may contribute little or no improvement to the forecast accuracy. We incrementally construct sets of sensors until we arrive at a set for which no superset produces a better forecast. Then we construct a successive series of subsets, such that forecast accuracy degrades slowly. As each sensor is removed, on the one hand, the forecast error increases, so the energy costs may increase for a given controller. On the other hand, the costs for installing sensors and for computing models are reduced. By considering this tradeoff over the the series of sets, an optimal set of sensors can be found to be used with that controller

Accurately forecasting temperatures in smart buildings using fewer sensors

© 2017, Springer-Verlag London Ltd., part of Springer Nature. Forecasts of temperature in a “smart” building, i.e. one that is outfitted with sensors, are computed from data gathered by these sensors. Model predictive controllers can use accurate temperature forecasts to save energy by optimally using heating, ventilation and air conditioners while achieving comfort. We report on experiments from such a house. We select different sets of sensors, build a temperature model from each set, and compare the accuracy of these models. While a primary goal of this research area is to reduce energy consumption, in this paper, besides the cost of energy, we consider the cost of data collection and management. Our approach informs the selection of an optimal set of sensors for any model predictive controller to reduce overall costs, using any forecasting methodology. We use lasso regression with lagged observations, which compares favourably to previous methods using the same data