scholarly journals SEMANTIC GRAPH BASED TERM EXPANSION FOR SENTENCE-LEVEL SENTIMENT ANALYSIS

2020 ◽  
pp. 647-655
Author(s):  
Mohammed Maree ◽  
Mujahed Eleyat
Keyword(s):  
Sentiment Analysis ◽  
Real World ◽  
Gold Standard ◽  
Semantic Networks ◽  
Prototype System ◽  
Analysis Techniques ◽  
Negative Sentence ◽  
Sentence Level ◽  
Semantic Orientation

The semantic orientation (also referred to as prior polarity) of a word plays an important role in automatic sentence-level sentiment analysis. Several approaches have been proposed wherein a lexicon of words marked with their polarities is exploited to infer the meaning of sentences. However, relying on prior word polarity may produce inaccurate decisions. This is because we may find negative-sentence sentiments that include words with positive prior polarities or vice versa. In this article, we propose an approach to sentence-level sentiment analysis that exploits knowledge encoded in heavy-weight semantic graphs to assist in discovering the meaning of a word in the context of the sentence where it appears. In this context, we build contextual semantic networks for indexing sentences and expand them with semantically/lexically-relevant terms in an attempt to disambiguate the meanings of word mentions in sentences. In order to verify the effectiveness of the proposed approach, we have developed a prototype system using a real-world dataset that contains 46830 sentiment sentences along with a gold-standard that comprises 10000 movie reviews that are labelled under five sentiment categories (very negative, negative, neutral, positive, very positive). Findings indicate that enriching the semantic graphs of sentiment sentences with NOUN-based synonyms and hypernyms has improved the overall quality of baseline sentiment analysis techniques.

scholarly journals Textual Analysis and Sentiment Analysis in Accounting

2021 ◽  
Vol 24 (2) ◽  
pp. 168-183
Author(s):  
Juan L. Gandía ◽  
David Huguet
Keyword(s):  
Sentiment Analysis ◽  
Textual Analysis ◽  
Financial Information ◽  
Digital Tools ◽  
Future Research ◽  
Analysis Techniques ◽  
Additional Information ◽  
New Perspective ◽  
Medios De Comunicacion

A pesar del relativamente escaso uso de técnicas de análisis textual y de análisis del sentimiento en finanzas y contabilidad, éstas tienen un gran potencial en contabilidad, tanto por el elevado volumen de documentos utilizados para la comunicación de información financiera como por el crecimiento en el uso de herramientas digitales y medios de comunicación social. En este sentido, estas técnicas de análisis pueden ayudar a los investigadores a analizar pistas ocultas o buscar información adicional a la observada a través de los estados financieros, incrementando la cantidad y calidad de la información tradicionalmente utilizada, y proporcionando una nueva perspectiva de análisis. Por ello, el objetivo de este estudio es realizar una revisión del uso del análisis textual y del análisis del sentimiento en contabilidad. Tras presentar los conceptos de análisis textual y análisis del sentimiento y justificar teóricamente su papel en la investigación en contabilidad, llevamos a cabo una revisión de la literatura previa en el uso de estas técnicas en finanzas y contabilidad y describimos las principales técnicas de análisis del sentimiento, así como el procedimiento a seguir para el uso de esta metodología. Finalmente, sugerimos tres líneas de investigación futura que pueden beneficiarse del uso del análisis textual y del análisis del sentimiento. In spite of the relatively scarce use of textual analysis and sentiment analysis techniques in finance and accounting, they have great potential in accounting, both because of the volume of documents used for the communication of information and due to the growth in the use of digital tools and social media. In that regard, these techniques of analysis may help researchers to analyse hidden clues or look for additional information to that one observed through financial information, increasing the quantity and quality of the information traditionally used, and providing a new perspective of analysis. The aim of this study is to review the use of textual analysis and sentiment analysis in accounting. After presenting the concepts of textual analysis and sentiment analysis and expose their interest in accounting, we perform a review of the previous literature on the use of these techniques in finance and accounting and describe the main techniques of sentiment analysis, as well as the procedure to be followed for the use of this methodology. Finally, we suggest three lines of future research that may benefit from the use of textual and sentiment analysis.

Measuring bilingual corpus comparability

2018 ◽  
Vol 24 (4) ◽  
pp. 523-549 ◽  
Author(s):  
BO LI ◽  
ERIC GAUSSIER ◽  
DAN YANG
Keyword(s):  
Real World ◽  
Gold Standard ◽  
The Real ◽  
Comparable Corpora ◽  
Bilingual Dictionary ◽  
Bilingual Lexicon ◽  
Real World Application ◽  
Parallel Resources

AbstractComparable corpora serve as an important substitute for parallel resources in cases of under-resourced language pairs. Previous work mostly aims to find a better strategy to exploit existing comparable corpora, while ignoring the variety in corpus quality. The quality of comparable corpora affects a lot its usability in practice, a fact that has been justified by several studies. However, researchers have not been able to establish a widely accepted and fully validated framework to measure corpus quality. We will thus investigate in this paper a comprehensive methodology to deal with the quality of comparable corpora. To be exact, we will propose several comparability measures and a quantitative strategy to test those measures. Our experiments show that the proposed comparability measure can capture gold-standard comparability levels very well and is robust to the bilingual dictionary used. Moreover, we will show in the task of bilingual lexicon extraction that the proposed measure correlates well with the performance of the real world application.

Learning Algorithms of Sentiment Analysis

2022 ◽  
pp. 176-194
Author(s):  
Suania Acampa ◽  
Ciro Clemente De Falco ◽  
Domenico Trezza
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Scientific Community ◽  
Learning Algorithms ◽  
Learning Approaches ◽  
Italian Language ◽  
Analysis Techniques ◽  
Stratified Sample

The uncritical application of automatic analysis techniques can be insidious. For this reason, the scientific community is very interested in the supervised approach. Can this be enough? This chapter aims to these issues by comparing three machine learning approaches to measuring the sentiment. The case study is the analysis of the sentiment expressed by the Italians on Twitter during the first post-lockdown day. To start the supervised model, it has been necessary to build a stratified sample of tweets by daily and classifying them manually. The model to be test provides for further analysis at the end of the process useful for comparing the three models: index will be built on the tweets processed with the aim of detecting the goodness of the results produced. The comparison of the three algorithms helps the authors to understand not only which is the best approach for the Italian language but tries to understand which strategy is to verify the quality of the data obtained.

scholarly journals Automatic generation of lexica for sentiment polarity shifters

2020 ◽  
pp. 1-27 ◽  
Author(s):  
Marc Schulder ◽  
Michael Wiegand ◽  
Josef Ruppenhofer
Keyword(s):  
Sentiment Analysis ◽  
Gold Standard ◽  
Automatic Generation ◽  
Human Language ◽  
High Quality ◽  
Closed Class ◽  
Annotation Costs ◽  
Polarity Classification ◽  
Semantic Resources

Abstract Alleviating pain is good and abandoning hope is bad. We instinctively understand how words like alleviate and abandon affect the polarity of a phrase, inverting or weakening it. When these words are content words, such as verbs, nouns, and adjectives, we refer to them as polarity shifters. Shifters are a frequent occurrence in human language and an important part of successfully modeling negation in sentiment analysis; yet research on negation modeling has focused almost exclusively on a small handful of closed-class negation words, such as not, no, and without. A major reason for this is that shifters are far more lexically diverse than negation words, but no resources exist to help identify them. We seek to remedy this lack of shifter resources by introducing a large lexicon of polarity shifters that covers English verbs, nouns, and adjectives. Creating the lexicon entirely by hand would be prohibitively expensive. Instead, we develop a bootstrapping approach that combines automatic classification with human verification to ensure the high quality of our lexicon while reducing annotation costs by over 70%. Our approach leverages a number of linguistic insights; while some features are based on textual patterns, others use semantic resources or syntactic relatedness. The created lexicon is evaluated both on a polarity shifter gold standard and on a polarity classification task.

Clobex Spray Improves Psoriasis, Quality of Life in Real-World Study

2007 ◽  
Vol 38 (4) ◽  
pp. 26
Author(s):  
DAMIAN MCNAMARA
Keyword(s):  
Quality Of Life ◽  
Real World

PENGEMBANGAN MEDIA PEMBELAJARAN INTERAKTIF TEKNIK PEMESINAN BUBUT

2020 ◽  
Vol 6 (2) ◽  
pp. 169
Author(s):  
Philip Nababan ◽  
Efendi Napitupulu ◽  
R Mursid
Keyword(s):  
Student Learning ◽  
Learning Outcomes ◽  
Interactive Learning ◽  
Descriptive Analysis ◽  
Student Learning Outcomes ◽  
Field Trials ◽  
Student Responses ◽  
Analysis Techniques ◽  
Qualitative Descriptive

Abstrak: Penelitian ini bertujuan untuk: (1) Mengetahui tanggapan siswa terhadap kualitas media pembelajaran interaktif pada pembelajaran Teknik Pemesinan Bubut. (2) Mengetahui keefektifan media pembelajaran interaktif pada pembelajaran Teknik Pemesinan Bubut pada siswa program keahlian Teknik Pemesinan. Jenis penelitian ini adalah penelitian pengembangan. Data tentang kualitas produk pengembangan ini dikumpulkan dengan angket dan dianalisis dengan teknik analisis deskriptif kualiatatif. Hasil penelitian menunjukkan bahwa; (1) uji ahli materi pelajaran Teknik Pemesinan Bubut berada pada kualifikasi sangat baik (88,92%), (2) uji ahli desain pembelajaran berada pada kualifikasi sangat baik (85,21%), (3) uji ahli rekayasa perangkat lunak berada pada kualifikasi sangat baik (84,03%), (4) uji coba perorangan berada pada kualifikasi sangat baik (88,75%), (5) uji coba kelompok kecil berada pada kualifikasi sangat baik (91,35%) dan (5) uji coba lapangan berada pada kualifikasi sangat baik (88,31%). Hasil pengujian hipotesis membuktikan bahwa terdapat perbedaan antara hasil belajar siswa yang menggunakan media pembelajaran interaktif  dengan hasil belajar siswa yang menggunakan buku teks. Hal ini ditunjukkan dengan hasil pengolahan data diperoleh  thitung sebesar 4,68 dan ttabel sebesar 1,67 pada taraf kepercayaan 95 persen. Maka diperoleh bahwa thitung> ttabel. Disimpulkan bahwa  hasil belajar siswa yang menggunakan media pembelajaran interaktif dengan efektifitas sebesar 72,77 %. lebih tinggi dari hasil belajar siswa yang diajar dengan pembelajaran menggunakan buku teks dengan efektifitas sebesar 62,13%. Kata Kunci: media pembelajaran interaktif, teknik pemesinan bubut Abstract: This study aims to: (1) Determine student responses to the quality of interactive learning media on learning Lathe Machining Techniques. (2) Knowing the effectiveness of interactive learning media on learning of Machining Lathe in students of Machining Engineering expertise program. This type of research is development research. Data about the quality of this development product was collected by a questionnaire and analyzed by qualitative descriptive analysis techniques. The results showed that; (1) Lathe machining engineering subject matter expert test is in very good qualification (88.92%), (2) learning design expert test is in very good qualification (85.21%), (3) software engineering expert test is in in very good qualifications (84.03%), (4) individual trials were in very good qualifications (88.75%), (5) small group trials were in very good qualifications (91.35%) and (5 ) field trials are in very good qualifications (88.31%). Hypothesis testing results prove that there are differences between student learning outcomes using interactive learning media with student learning outcomes using textbooks. This is indicated by the results of data processing obtained by tcount of 4.68 and ttable of 1.67 at a confidence level of 95 percent. Then it is obtained that tcount> ttable. It was concluded that student learning outcomes using interactive learning media with an effectiveness of 72.77%. higher than student learning outcomes taught by learning to use textbooks with an effectiveness of 62.13%. Keywords: interactive learning media, lathe machining techniques

EXPLORING HEALTHCARE ARCHITECTURE THROUGH THE MEDIUM OF FILM: Motives and Techniques

2014 ◽  
Vol 8 (1) ◽  
pp. 29
Author(s):  
Stephen Verderber
Keyword(s):  
Real World ◽  
The Real ◽  
Healthcare Settings ◽  
Interdisciplinary Field ◽  
Body Of Knowledge ◽  
Behavior Setting ◽  
Physical Environments ◽  
Scant Attention ◽  
History Of

The interdisciplinary field of person-environment relations has, from its origins, addressed the transactional relationship between human behavior and the built environment. This body of knowledge has been based upon qualitative and quantitative assessment of phenomena in the “real world.” This knowledge base has been instrumental in advancing the quality of real, physical environments globally at various scales of inquiry and with myriad user/client constituencies. By contrast, scant attention has been devoted to using simulation as a means to examine and represent person-environment transactions and how what is learned can be applied. The present discussion posits that press-competency theory, with related aspects drawn from functionalist-evolutionary theory, can together function to help us learn of how the medium of film can yield further insights to person-environment (P-E) transactions in the real world. Sampling, combined with extemporary behavior setting analysis, provide the basis for this analysis of healthcare settings as expressed throughout the history of cinema. This method can be of significant aid in examining P-E transactions across diverse historical periods, building types and places, healthcare and otherwise, otherwise logistically, geographically, or temporally unattainable in real time and space.

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Text Documents ◽  
Customer Churn ◽  
Learning Classifier ◽  
Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

Impact of Atopic Dermatitis Lesion Location on Quality of Life in Adult Patients in a Real-world Study

2020 ◽  
Vol 19 (10) ◽  
pp. 943-948
Author(s):  
Peter Lio ◽  
Andreas Wollenberg ◽  
Jacob Thyssen ◽  
Evangeline Pierce ◽  
Maria Rueda ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Atopic Dermatitis ◽  
Real World ◽  
Adult Patients ◽  
Lesion Location

UPAYA MENINGKATKAN KOMPETENSI GURU DALAM MENYUSUN RENCANA PELAKSANAAN PEMBELAJARAN MELALUI BIMBINGAN BERKELANJUTAN DI SD NEGERI 017 KUNTO DARUSSALAM TAHUN 2017

2020 ◽  
Author(s):  
Sri Winarsih
Keyword(s):  
Learning Strategies ◽  
Learning Process ◽  
School Principal ◽  
Initial Condition ◽  
Research Subjects ◽  
Analysis Techniques ◽  
Pedagogical Competence ◽  
Treatment Data ◽  
The Subject

This study aims to determine the appropriate steps in carrying out academic supervision so as to be able to improve the pedagogical competence of teachers, especially in the learning process which in turn will affect the improvement of the quality of education.The study was conducted in two cycles. Each cycle has different planning, implementation, observation and reflection. Research subjects of the principal and teacher. The school principal with his academic supervision measures, while the Kunto Darussalam Elementary School 017 teacher as an object as well as the subject in providing academic supervision treatment. Data collection techniques through class supervision with stages of supervising teachers in the learning process and observation of classroom learning, to record important events related to research, especially at the time of the processlearning takes place.Data analysis techniques that guide data processing using a percentage (%) of achievement with 100 constants. And to see the interpertation using score interpertation criteria to strengthen the interpretation in conclusions as follows: 80% - 100% (Very Good), 66% - 79 % (Good), 56% - 65% (Enough), and 40% - 55% (Less).The results showed that the ability of teachers in the implementation of the learning process experienced an increase in the percentage at each stage, from the first cycle reached an average of 63% (sufficient) and in the second cycle reached an average of 68% (good). There is an increase in teacher's ability by 5% from cycle I. In detail there is a significant increase in the initial condition of the school when compared to the final condition in the second cycle. The accuracy of teachers entering the class increased by 48%, the use of learning media increased by 32%, varied methods increased by 31%, and learning strategies increased by 36%.

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