Polarity of opinions about a public person in Ecuador

The present investigation is the study of opinion miningtechniques, focused on obtaining information from a public figurein Ecuador, determining signs of polarity for your management ina positive, negative or neutral way, a result that will allow saidcharacter public to make decisions about their actions based on animage of service to the community. The extraction of opinions insocial networks and techniques based on Human LanguageTechnologies enabled the interpretation of polarized data byspecifying parameters of relevance to the resulting opinion focusedon decision making, processing that adapts to the newcommunication formats achieving the interpretation andassessment of opinion. Social networks was the platform for thecapture of texts by means of an API, which after the processing ofthe natural language obtained results of indications of thepopularity of the character

Business Versus Complexity

AbstractAccording to our view, business is an economic category consists of products that are sold in markets that have a significant contribution to a company's business portfolio. At a company's level, businesses have a competitive position in the distribution of its resources to the consumption of inputs. In terms of management decision the allocation of material resources, labor and capital reflects its market position and determine the hierarchy of the various products that have turnover. The cost of the Knowledge of information produced determine the internal complexity of operation at an organization. Complex business models started to be represented everywhere around us the relationships between various entities that adapt and respond to the dynamics of internal and external environment. Most often these models operate in networks. Information system (IS) can be a modern solution to explain the above-mentioned complexity. The information in this case to capture the interactions between production function and the marketing in a subsystem which dialogues between its parts that are found in the interaction within the system to create knowledge and value this knowledge in costs and outcome. This paper focuses on information systems in an organization of great complexity as Google. Inc. its financial accountancy consideration and also the effects measures of Information System (IS) used in Google Inc

Targeted Investment for Food Access

This project focuses on modeling access to food locations by identifying the most critical roadway links in a transportation network. This project extends the Critical Closeness Accessibility (CCA) measure developed by Novak and Sullivan (2014) to identify the roadway infrastructure components that are most critical with respect to food accessibility. Specifically, origin and destination weighting are included for the application of food security, where origins are weighted according to household vulnerability and destinations are weighted by retail-grocery square footage. The CCA is further extended by calibrating the trip impedance constant, ω, in the original formulation of the CCA with actual grocery-shopping data from the National Household Travel Survey. This calibration modifies the functional form of the accessibility measure to address trips focused on food access and thus incorporates realistic travel expectations for retail grocery familiarity of households. The project also provides a unique method for estimating household level vulnerability characteristics using population synthesis. The modification of the CCA to address food accessibility can be used to support more targeted investment in transportation assets, as the CCA is indexed to specific roadway links in the network. The methodology is demonstrated using the Travel Demand Model of Chittenden County, Vermont

Quantifying the Impact that New Capital Projects Will Have on Roadway Snow and Ice Control Operations

In recent years, many states have experienced heavy burdens on their snow and ice control budgets. Increases in winter/spring precipitation results in increased costs to state DOTs for winter roadway maintenance materials (salt, sand, chemicals, etc.), plow operator time, equipment maintenance and replacement budgets, and fuel use. As state DOTs adjust to climate conditions that include not only more precipitation, but more severe and unpredictable weather events, it will become increasingly important to integrate the cost of roadway snow and ice control (RSIC) operations into their capital-project planning processes. The overall goal of this project was to support state DOTs’ operations & maintenance efforts by developing an automated method for quantifying the expected impact that new capital projects will have on RSIC operations. The effects of a new suburban roadway were found to be the most significant, requiring 266 vehicle-minutes of travel along with almost 40 minutes of additional service time or one additional fleet truck for each mile of new roadway. The results and findings of this research have implications for short-term funding allocations for RSIC operations staff and for long-term consideration of RSIC in the highway planning and design processes. The findings of this project provide defensible data for operations staff to advocate for increases in funding to offset the increased RSIC burden when a project is completed. The calculation tool created incorporates all of the results above into a MS Excel decision support platform, providing quick estimates of the monetary impact of a variety of major highway project types

Web Service SWePT: A Hybrid Opinion Mining Approach

[EN] The increasing use of social networks and online sites where people can express their opinions has created a growing interest in Opinion Mining. One of the main tasks of Opinion Mining is to determine whether an opinion is positive or negative. Therefore, the role of the feelings expressed on the web has become crucial, mainly due to the concern of businesses and government to automatically identify the semantic orientation of the views of customers or citizens. This is also a concern, in the area of health to identify psychological disorders. This research focuses on the development of a web application called SWePT (Web Service for Polarity detection in Spanish Texts), which implements the Sequential Minimal Optimization (SMO) algorithm, extracting its features from an affective lexicon in Mexican Spanish. For this purpose, a corpus and an affective lexicon in Mexican Spanish were created. The experiments using three (positive, neutral, negative) and five categories (very positive, positive, neutral, negative, and very negative) allow us to demonstrate the effectiveness of the presented method. SWePT has also been implemented in the Emotion-bracelet interface, which shows the opinion of a user graphically.This work has been partially supported by the Sectorial Fund CONACyT-INEGI: Project with ref. 208471, INFOTEC, Mexico. And, also by the project CNDT-PYR2015-0016, CENIDET, Mexico. The work of the third author was in the framework of the SomEMBED MINECO TIN2015-71147-C2-1-P research project. The National Council for Science and Technology (CONACyT Mexico) has funded the research work of Delia Irazu Hernandez Farias (Grant No. 218109/313683 CVU-369616).Baca-Gomez, YR.; Martínez, A.; Rosso, P.; Estrada Esquivel, H.; Hernandez-Farias, DI. (2016). Web Service SWePT: A Hybrid Opinion Mining Approach. Journal of Universal Computer Science. 22(5):671-690. https://doi.org/10.3217/jucs-022-05-067167169022

Quantifying the Impact that New Capital Projects Will Have on Roadway Snow and Ice Control Operations

MnDOT No. 06428In recent years, many states have experienced heavy burdens on their snow and ice control budgets. Increases in winter/spring precipitation results in increased costs to state departments of transportation (DOTs) for winter roadway maintenance materials (salt, sand, chemicals, etc.), plow operator time, equipment maintenance and replacement budgets, and fuel use. As state DOTs adjust to climate conditions that include not only more precipitation, but more severe and unpredictable weather events, it will become increasingly important to integrate the cost of roadway snow and ice control (RSIC) operations into their capital-project planning processes. The overall goal of this project was to support state DOTs\u2019 operations & maintenance efforts by developing an automated method for quantifying the expected impact that new capital projects will have on RSIC operations. The effects of a new suburban roadway were found to be the most significant, requiring 266 vehicle-minutes of travel along with almost 40 minutes of additional service time or one additional fleet truck for each mile of new roadway. The results and findings of this research have implications for short-term funding allocations for RSIC operations staff and for long-term consideration of RSIC in the highway planning and design processes. The findings of this project provide defensible data for operations staff to advocate for increases in funding to offset the increased RSIC burden when a project is completed. The calculation tool created incorporates all of the results above into a MS Excel decision support platform, providing quick estimates of the monetary impact of a variety of major highway project types

Evaluating the impact of social-media on sales forecasting: a quantitative study of worlds biggest brands using Twitter, Facebook and Google Trends

In the world of digital communication, data from online sources such as social networks might provide additional information about changing consumer interest and significantly improve the accuracy of forecasting models. In this thesis I investigate whether information from Twitter, Facebook and Google Trends have the ability to improve daily sales forecasts for companies with respect to the forecasts from transactional sales data only. My original contribution to this domain, exposed in the present thesis, consists in the following main steps: 1. Data collection. I collected Twitter, Facebook and Google Trends data for the period May 2013 May 2015 for 75 brands. Historical transactional sales data was supplied by Certona Corporation. 2. Sentiment analysis. I introduced a new sentiment classification approach based on combining the two standard techniques (lexicon-based and machine learning based). The proposed method outperforms the state-of-the-art approach by 7% in F-score. 3. Identification and classification of events. I proposed a framework for events detection and a robust method for clustering Twitter events into different types based on the shape of the Twitter volume and sentiment peaks. This approach allows to capture the varying dynamics of information propagation through the social network. I provide empirical evidence that it is possible to identify types of Twitter events that have significant power to predict spikes in sales. 4. Forecasting next day sales. I explored linear, non-linear and cointegrating relationships between sales and social-media variables for 18 brands and showed that social-media variables can improve daily sales forecasts for the majority of brands by capturing factors, such as consumer sentiment and brand perception. Moreover, I identified that social-media data without sales information, can be used to predict sales direction with the accuracy of 63%. The experts from the industry consider the results obtained in this thesis to be valuable and useful for decision making and for making strategic planning for the future