scholarly journals Meta-Learner for Amharic Sentiment Classification

2021 ◽  
Vol 11 (18) ◽  
pp. 8489
Author(s):  
Girma Neshir ◽  
Andreas Rauber ◽  
Solomon Atnafu
Keyword(s):  
Sentiment Analysis ◽  
Performance Indicator ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Training Data ◽  
Policy Makers ◽  
The Past ◽  
Classification Framework

The emergence of the World Wide Web facilitates the growth of user-generated texts in less-resourced languages. Sentiment analysis of these texts may serve as a key performance indicator of the quality of services delivered by companies and government institutions. The presence of user-generated texts is an opportunity for assisting managers and policy-makers. These texts are used to improve performance and increase the level of customers’ satisfaction. Because of this potential, sentiment analysis has been widely researched in the past few years. A plethora of approaches and tools have been developed—albeit predominantly for well-resourced languages such as English. Resources for less-resourced languages such as, in this paper, Amharic, are much less developed. As a result, it requires cost-effective approaches and massive amounts of annotated training data, calling for different approaches to be applied. This research investigates the performance of a combination of heterogeneous machine learning algorithms (base learners such as SVM, RF, and NB). These models in the framework are fused by a meta-learner (in this case, logistic regression) for Amharic sentiment classification. An annotated corpus is provided for evaluation of the classification framework. The proposed stacked approach applying SMOTE on TF-IDF characters (1,7) grams features has achieved an accuracy of 90%. The overall results of the meta-learner (i.e., stack ensemble) have revealed performance rise over the base learners with TF-IDF character n-grams.

Machine Learning Approaches for Sentiment Analysis

2014 ◽  
pp. 193-208 ◽  
Author(s):  
Basant Agarwal ◽  
Namita Mittal
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Knowledge Based ◽  
Semantic Orientation

Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.

Machine Learning Approaches for Sentiment Analysis

Big Data ◽  
2016 ◽  
pp. 1917-1933
Author(s):  
Basant Agarwal ◽  
Namita Mittal
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Knowledge Based ◽  
Semantic Orientation

Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.

scholarly journals Modified framework for sarcasm detection and classification in sentiment analysis

2019 ◽  
Vol 13 (3) ◽  
pp. 1175 ◽  
Author(s):  
Mohd Suhairi Md Suhaimin ◽  
Mohd Hanafi Ahmad Hijazi ◽  
Rayner Alfred ◽  
Frans Coenen
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Classification Performance ◽  
Sentiment Classification ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Social Media Data ◽  
Classification Framework ◽  
Media Data ◽  
F Measure

<span>Sentiment analysis is directed at identifying people's opinions, beliefs, views and emotions in the context of the entities and attributes that appear in text. The presence of sarcasm, however, can significantly hamper sentiment analysis. In this paper a sentiment classification framework is presented that incorporates sarcasm detection. The framework was evaluated using a non-linear Support Vector Machine and Malay social media data. The results obtained demonstrated that the proposed sarcasm detection process could successfully detect the presence of sarcasm in that better sentiment classification performance was recorded. A best average F-measure score of 0.905 was recorded using the framework; a significantly better result than when sentiment classification was performed without sarcasm detection.</span>

scholarly journals Orthopedic Trauma and Recovery of Quality of Life: An Overview of the Literature

2016 ◽  
Vol 7 ◽  
pp. CMTIM.S25875 ◽  
Author(s):  
Khalid Hamid ◽  
Pramin Raut ◽  
Bessam Ahmed ◽  
William Eardley
Keyword(s):  
Quality Of Life ◽  
Clinical Success ◽  
Cost Effective ◽  
Fundamental Aspect ◽  
Orthopedic Trauma ◽  
Radiographic Evidence ◽  
The Past ◽  
Effective Care ◽  
Surgeon Performance

Assessment of clinical success by radiographic evidence of fracture union and surgeon-rated performance following recovery are the outcome tools of the past. Patients are now involved in the assessment of both surgeon performance and the capacity of the institutions in which they are treated to provide rehabilitation following injury. This population is increasingly involved in trials to guide most appropriate and cost-effective care. With healthcare resources globally under pressure, research focus on patient-rated outcome per unit expenditure is central to orthopedic evidence-based practice. In this era of patient-focused assessment and healthcare economics, quality of life and alterations in this status are central as outcome measures. In order to quantify the return of quality of life following injury, we present a review of the literature pertaining to this fundamental aspect of orthopedic trauma patient care.

Probable impact of the 1994 elections on laboratory medicine

1995 ◽  
Vol 41 (8) ◽  
pp. 1223-1227
Author(s):  
H S Foster
Keyword(s):  
Clinical Laboratory ◽  
Quality Of Healthcare ◽  
Healthcare Services ◽  
Policy Makers ◽  
Quarter Century ◽  
The Past ◽  
Probable Impact ◽  
Clinical Laboratory Improvement Amendments

Abstract During the past quarter century, federal health policy makers concerned themselves with: (a) improving the quality of healthcare delivered to the American public; (b) increasing access to needed healthcare services; and (c) curtailing the escalating cost of such services. These goals led Congress to expand the role of the federal government in regulating the delivery of healthcare. The enactment of the Clinical Laboratory Improvement Amendments of 1988 (CLIA '88) was a significant and widely discussed example of how Congress, when controlled by the Democrats, sought to correct healthcare problems and achieve federal objectives. In November 1994, the Republicans won majorities in both the Senate and the House, promising to reduce the federal government's power. Many now believe that CLIA '88, or significant parts of it, could be substantially modified as part of this effort. This paper addresses the developments that led the Democrats to seek enactment of CLIA '88 and the likely arguments that may be offered by the Republicans to lessen the rigor and scope of the law.

scholarly journals A Statistical Parsing Framework for Sentiment Classification

2015 ◽  
Vol 41 (2) ◽  
pp. 293-336 ◽  
Author(s):  
Li Dong ◽  
Furu Wei ◽  
Shujie Liu ◽  
Ming Zhou ◽  
Ke Xu
Keyword(s):  
Sentiment Analysis ◽  
Sentiment Classification ◽  
Training Data ◽  
Data Sets ◽  
Syntactic Parsing ◽  
Benchmark Data ◽  
Sentence Level ◽  
Statistical Parsing ◽  
Parse Trees ◽  
Context Free

We present a statistical parsing framework for sentence-level sentiment classification in this article. Unlike previous works that use syntactic parsing results for sentiment analysis, we develop a statistical parser to directly analyze the sentiment structure of a sentence. We show that complicated phenomena in sentiment analysis (e.g., negation, intensification, and contrast) can be handled the same way as simple and straightforward sentiment expressions in a unified and probabilistic way. We formulate the sentiment grammar upon Context-Free Grammars (CFGs), and provide a formal description of the sentiment parsing framework. We develop the parsing model to obtain possible sentiment parse trees for a sentence, from which the polarity model is proposed to derive the sentiment strength and polarity, and the ranking model is dedicated to selecting the best sentiment tree. We train the parser directly from examples of sentences annotated only with sentiment polarity labels but without any syntactic annotations or polarity annotations of constituents within sentences. Therefore we can obtain training data easily. In particular, we train a sentiment parser, s.parser, from a large amount of review sentences with users' ratings as rough sentiment polarity labels. Extensive experiments on existing benchmark data sets show significant improvements over baseline sentiment classification approaches.

scholarly journals Assessing Resilience of Urban Critical Infrastructure Networks: A Case Study of Ahvaz, Iran

2020 ◽  
Vol 12 (9) ◽  
pp. 3691 ◽  
Author(s):  
Hadi Alizadeh ◽  
Ayyoob Sharifi
Keyword(s):  
Service Provision ◽  
Design Pattern ◽  
Critical Infrastructure ◽  
Policy Makers ◽  
Infrastructure Networks ◽  
The Past ◽  
The City ◽  
Different Parts

Cities around the world increasingly recognize the need to build on their resilience to deal with the converging forces of urbanization and climate change. Given the significance of critical infrastructure for maintaining quality of life in cities, improving their resilience is of high importance to planners and policy makers. The main purpose of this study is to spatially analyze the resilience of water, electricity, and gas critical infrastructure networks in Ahvaz, a major Iranian city that has been hit by various disastrous events over the past few years. Towards this goal, we first conducted a two-round Delphi survey to identify criteria that can be used for determining resilience of critical infrastructure networks across different parts of the city. The selected criteria that were used for spatial analysis are related to the physical texture, the design pattern, and the scale of service provision of the critical infrastructure networks. Results showed that, overall, critical infrastructure networks in Ahvaz do not perform well against the measurement criteria. This is specially the case in Regions 1, 2, 4, and 6, which are characterized by issues such as old and centralized infrastructure networks and high levels of population density. The study highlights the need to make improvements in terms of the robustness, redundancy, and flexibility of the critical infrastructure networks in the city.

scholarly journals Multiclass Sentiment Analysis of Social Media Data using Neural Networks

2019 ◽  
Vol 9 (1S3) ◽  
pp. 57-62
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Binary Classification ◽  
Multiclass Classification ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Training Dataset ◽  
Data Sets

Sentiment analysis, also known as Opinion Mining is one of the hottest topic Nowadays. in various social networking sites is one of the hottest topic and field nowadays. Here, we are using Twitter, the biggest web destinations for people to communicate with each other to perform the sentiment analysis and opinion mining by extracting the tweets by various users. The users can post brief text updates in twitter as it only allows 140 characters in one text message. Hashtags helps to search for tweets dealing with the specified subject. In previous researches, binary classification usually relies on the sentiment polarity(Positive , Negative and Neutral). The advantage is that multiple meaning of the same world might have different polarity, so it can be easily identified. In Multiclass classification, many tweets of one class are classified as if they belong to the others. The Neutral class presented the lowest precision in all the researches happened in this particular area. The set of tweets containing text and emoticon data will be classified into 13 classes. From each tweet, we extract different set of features using one hot encoding algorithm and use machine learning algorithms to perform classification. The entire tweets will be divided into training data sets and testing data sets. Training dataset will be pre-processed and classified using various Artificial Neural Network algorithms such as Reccurent Neural Network, Convolutional Neural Network etc. Moreover, the same procedure will be followed for the Text and Emoticon data. The developed model or system will be tested using the testing dataset. More precise and correct accuracy can be obtained or experienced using this multiclass classification of text and emoticons. 4 Key performance indicators will be used to evaluate the effectiveness of the corresponding approach.

Components of the Economic Burden of Serious Mental Illness in the US

US Neurology ◽  
2009 ◽  
Vol 05 (01) ◽  
pp. 10 ◽  
Author(s):  
Thomas R Insel ◽  
Michael Schoenbaum ◽  
Philip S Wang ◽  
◽  
◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Mental Disorders ◽  
Mental Health Treatment ◽  
Cost Effective ◽  
Health Treatment ◽  
Effective Strategies ◽  
The Past ◽  
The Us

Mental disorders impose considerable socioeconomic costs due to their episodic/chronic nature, their relatively early ages at onset, and the highly disabling nature of inadequately treated mental illness. Despite substantial increases in the volume of mental health treatment for disorders in the past two decades, particularly pharmacotherapies, the level of morbidity and mortality from these disorders does not appear to have changed substantially over this period. Improving outcomes will require the development and use of more efficacious treatments for mental disorders. Likewise, implementation of cost-effective strategies to improve the quality of existing care for these disabling conditions is required.

scholarly journals Representing Small Commercial Building Faults in EnergyPlus, Part II: Model Validation

Buildings ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 239 ◽  
Author(s):  
Janghyun Kim ◽  
Stephen Frank ◽  
Piljae Im ◽  
James E. Braun ◽  
David Goldwasser ◽  
...  
Keyword(s):  
Cost Effective ◽  
Research Area ◽  
Fault Detection And Diagnosis ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Fault Models ◽  
Commercial Building ◽  
Data Set ◽  
Modeling Software ◽  
Detection And Diagnosis

Automated fault detection and diagnosis (AFDD) tools based on machine-learning algorithms hold promise for lowering cost barriers for AFDD in small commercial buildings; however, access to high-quality training data for such algorithms is often difficult to obtain. To fill the gap in this research area, this study covers the development (Part I) and validation (Part II) of fault models that can be used with the building energy modeling software EnergyPlus® and OpenStudio® to generate a cost-effective training data set for developing AFDD algorithms. Part II (this paper) first presents a methodology of validating fault models with OpenStudio and then presents validation results, which are compared against measurements from a reference building. We discuss the results of our experiments with eight different faults in the reference building (a total of 39 different baseline and faulted scenarios), including our methodology for using fault models along with the reference building model to simulate the same faulted scenarios. Then, we present validation of the fault models by comparing results of simulations and experiments either quantitatively or qualitatively.

Sign in / Sign up

Export Citation Format

Share Document