scholarly journals Natural Language Processing for the Analysis of the Political Characterisation of Migration in the Croatian Political Discourse

2020 ◽  
Vol 22 (3) ◽  
pp. 517-532
Author(s):  
Gabriele De Luca ◽  
Marko Beck
Keyword(s):  
Machine Learning ◽  
Political Parties ◽  
Language Processing ◽  
Political Discourse ◽  
Research Process ◽  
Learning System ◽  
Liberal Party ◽  
War Of Independence ◽  
Political Analysis ◽  
Minority Party

This paper tackles the issue of analyst bias in performance of comparative political analyses on political discourse, by leveraging data and machine-learning over human prior knowledge. The case studied is characterization of the issue of migration in the Croatian political discourse, which was chosen arbitrarily. We developed a machine-learning system that identifies most prominent features in the Croatian political discourse, with regards to migration and were interested solo in comparative political analysis in political science. This system does not rely on human judgement on the part of the researchers, and can be thus considered to be “objective”, short of possible sampling or selection bias. It is replicable. If provided, the same dataset and algorithm used, same conclusions should be reached by any scientist. This result was achieved by creating a text corpus from news items and press releases extracted from the websites of Croatian political parties currently represented in the Parliament. Available and collected data consist of public announcements mainly from IDS (Istarski Demokratski Sabor / Istrian Democratic Assambly), SDSS (Samostalna Demokratska Srpska Stranka / Independed Democratic Serb Party) and HSLS (Hrvatska Socijalno Liberalna Stranka / Croatian Social Liberal Party). Data analyzed suggests three dominant phrases of the research process. All political parties had similar political stand towards pointed out issues. Three most significant phrases were determined. First phrase is related to words “Demography” and “Reduction” and finding suggest that most analyzed articles relates towards migration of Croatian citizens in connection to economic hardships of some kind. Phrase two is related to words “Border” and “Croatia-Serbia” which strongly indicates relation to migration and is related towards inter-Balkan migration, mostly connected with consequences of the Croatian War of Independence from 1990’s, and is of most interest to SDSS, a Serb minority party in Croatia. Phrase three is related towards Marrakesh Agreement (Global Compact for Safe, Orderly and Regular Migration), where most of analyzed data shows that parties have a constructive but ambivalent stance towards migration from the third countries. Research conducted on available data, shows that wide spread international migration is not in the focus of most Croatian political parties, while topics and interest for inter-Balkan and Croatian economic/political migration dominates Croatian political spectre

Pour une analyse politique du patronage

1974 ◽  
Vol 7 (3) ◽  
pp. 484-501 ◽  
Author(s):  
Raymond Hudon
Keyword(s):  
Political Parties ◽  
Political Competition ◽  
Liberal Party ◽  
Evolutionary Pattern ◽  
Political Analysis ◽  
Complex Process ◽  
Empirical Level ◽  
Client Relationship ◽  
Political Patronage ◽  
Different Characteristics

Towards a political analysis of patronagePolitical patronage is a phenomenon which has already been analysed and evaluated from many angles. An analytical model inspired by cybernetics is proposed here as a framework for the interpretation of the phenomenon. Such a model leads one to observe interactions between the different actors of a given society in terms of power relations. Using the ideas suggested by the model, the author describes patronage as a complex process in which a client relationship is established between patron and client, following which the former tries to alter his relationship with his opponents in political competition. Through the establishment of a client relationship, the patron helps to pull the client out from a certain state of weakness so as to obtain the means which the client wants for himself. Consequently, thanks to the means obtained by the client, which help him augment his power, the patron tries once more to alter his relations with his rivals in the political competition. In this sense, patronage permits a double transformation.On an empirical level two questions are posed. Can one trace an evolutionary pattern in the practice of patronage by political parties in Quebec between 1944 and 1972, and in what sense can patronage be defined? Does patronage have different characteristics depending on whether it is practised by the Liberal party or the Union nationale in the period under review?

scholarly journals Automatic Incident Triage in Radiation Oncology Incident Learning System

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 272
Author(s):  
Khajamoinuddin Syed ◽  
William Sleeman ◽  
Michael Hagan ◽  
Jatinder Palta ◽  
Rishabh Kapoor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Language Processing ◽  
Radiation Oncology ◽  
Learning System ◽  
Fine Tuning ◽  
Support Vector ◽  
Learning Approach ◽  
Veterans Health ◽  
Incident Reports

The Radiotherapy Incident Reporting and Analysis System (RIRAS) receives incident reports from Radiation Oncology facilities across the US Veterans Health Affairs (VHA) enterprise and Virginia Commonwealth University (VCU). In this work, we propose a computational pipeline for analysis of radiation oncology incident reports. Our pipeline uses machine learning (ML) and natural language processing (NLP) based methods to predict the severity of the incidents reported in the RIRAS platform using the textual description of the reported incidents. These incidents in RIRAS are reviewed by a radiation oncology subject matter expert (SME), who initially triages some incidents based on the salient elements in the incident report. To automate the triage process, we used the data from the VHA treatment centers and the VCU radiation oncology department. We used NLP combined with traditional ML algorithms, including support vector machine (SVM) with linear kernel, and compared it against the transfer learning approach with the universal language model fine-tuning (ULMFiT) algorithm. In RIRAS, severities are divided into four categories; A, B, C, and D, with A being the most severe to D being the least. In this work, we built models to predict High (A & B) vs. Low (C & D) severity instead of all the four categories. Models were evaluated with macro-averaged precision, recall, and F1-Score. The Traditional ML machine learning (SVM-linear) approach did well on the VHA dataset with 0.78 F1-Score but performed poorly on the VCU dataset with 0.5 F1-Score. The transfer learning approach did well on both datasets with 0.81 F1-Score on VHA dataset and 0.68 F1-Score on the VCU dataset. Overall, our methods show promise in automating the triage and severity determination process from radiotherapy incident reports.

scholarly journals Multilingual Lexicon based Approach for Real-Time Sentiment Analysis

2020 ◽  
Vol 9 (2) ◽  
pp. 984-989
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Political Parties ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Common People ◽  
Svm Algorithm

The information on WWW has mounted to a greater height, overriding to fledgling analysis in the direction of sentiments using Artificial Intelligence. Sentiment Analysis deals with the calculus exploration of sentiments, opinions and subjectivity. In this paper, multilingual tweets are analyzed for identifying the polarities of various political parties like AAP, BJP, Samajwadi, BSP and Congress; so that the users will get an idea that to which party they should give their vote. The data is being analyzed using Natural Language Processing. Using different smoothening techniques, noise is removed from data, classified by using Machine learning algorithms and then the accuracy of the system is gauged using various evaluation precision measures. The central premise of this research is to benignant common people and politicians both. For common people; is for deciding their precious vote, to which party to give will be good for themselves and nation too. For politicians; they will have an idea about themselves i.e. after seeking the polarities of different parties, the politicians will have an idea which party is preferable and which is not preferable, so that the politicians can work accordingly. The system shows comparison among VADER and SVM algorithm; and SVM algorithm showed 90% accuracy.

Pedagogical Indexed Arabic Text in Cloud E-Learning System

2017 ◽  
Vol 7 (1) ◽  
pp. 32-46 ◽  
Author(s):  
Nafaa Haffar ◽  
Mohsen Maraoui ◽  
Shadi Aljawarneh ◽  
Mohammed Bouhorma ◽  
Abdallah Altahan Alnuaimi ◽  
...  
Keyword(s):  
Language Processing ◽  
Keyword Search ◽  
Research Process ◽  
Arabic Language ◽  
Learning System ◽  
Arabic Text ◽  
E Learning ◽  
Application Profile ◽  
Search For Information ◽  
Specific Search

The Cloud E-Learning Systems for the Arabic language are relevant environments in many areas of training (teaching Arabic language) but also pose problems related to their creation tedious, costly in resources and time, and problems related to the search for information because of the increasing amount of information available and because of the methods of indexing, which is based on static methods such as keyword search that makes irrelevant the research process. For this, a new method of indexation is required. In this paper, a new Arabic text is proposed indexing approach using the creation of a new application profile of the LOM metadata schema (Learning Object Metadata) for the Arabic language. This profile includes the fields of LOM standard, and adds new fields for specific search information to Arabic language, and meets the needs of a teacher. Also, it's all using natural language processing tools like SAPA and AL-KHALIL.

scholarly journals A Hybrid Approach to Sentiment Sentence Classification in Suicide Notes

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8961 ◽  
Author(s):  
Sunghwan Sohn ◽  
Manabu Torii ◽  
Dingcheng Li ◽  
Kavishwar Wagholikar ◽  
Stephen Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Hybrid Approach ◽  
Sentiment Classification ◽  
Learning System ◽  
Rule Based ◽  
Rule Based System ◽  
System A ◽  
Suicide Notes ◽  
Matching Rules

This paper describes the sentiment classification system developed by the Mayo Clinic team for the 2011 I2B2/VA/Cincinnati Natural Language Processing (NLP) Challenge. The sentiment classification task is to assign any pertinent emotion to each sentence in suicide notes. We have implemented three systems that have been trained on suicide notes provided by the I2B2 challenge organizer–-a machine learning system, a rule-based system, and a system consisting of a combination of both. Our machine learning system was trained on re-annotated data in which apparently inconsistent emotion assignment was adjusted. Then, the machine learning methods by RIPPER and multinomial Naïve Bayes classifiers, manual pattern matching rules, and the combination of the two systems were tested to determine the emotions within sentences. The combination of the machine learning and rule-based system performed best and produced a micro-average F-score of 0.5640.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

Race and Gender

2020 ◽  
pp. 251-269 ◽  
Author(s):  
Timnit Gebru
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
The United States ◽  
Error Rates ◽  
Political Factors ◽  
Recidivism Rates ◽  
Race And Gender ◽  
Decision Tools ◽  
And Gender ◽  
Technical Solutions

This chapter discusses the role of race and gender in artificial intelligence (AI). The rapid permeation of AI into society has not been accompanied by a thorough investigation of the sociopolitical issues that cause certain groups of people to be harmed rather than advantaged by it. For instance, recent studies have shown that commercial automated facial analysis systems have much higher error rates for dark-skinned women, while having minimal errors on light-skinned men. Moreover, a 2016 ProPublica investigation uncovered that machine learning–based tools that assess crime recidivism rates in the United States are biased against African Americans. Other studies show that natural language–processing tools trained on news articles exhibit societal biases. While many technical solutions have been proposed to alleviate bias in machine learning systems, a holistic and multifaceted approach must be taken. This includes standardization bodies determining what types of systems can be used in which scenarios, making sure that automated decision tools are created by people from diverse backgrounds, and understanding the historical and political factors that disadvantage certain groups who are subjected to these tools.

Sign in / Sign up

Export Citation Format

Share Document