research-article

SentiML: functional annotation for multilingual sentiment analysis

Authors:

Marilena Di Bari,

Martin ThomasAuthors Info & Claims

DH-CASE '13: Proceedings of the 1st International Workshop on Collaborative Annotations in Shared Environment: metadata, vocabularies and techniques in the Digital Humanities

Article No.: 15, Pages 1 - 7

https://doi.org/10.1145/2517978.2517994

Published: 10 September 2013 Publication History

Abstract

Sentiment Analysis is the task of automatically identifying whether a text or a single sentence is intended to carry a positive or negative connotation. The commonly used Bag-of-Words approach that relies on counting positive and negative words, whose connotation is indicated by specially crafted sentiment dictionaries, is not ideal because it does not take into account the relations between words and how the connotation of single words changes according to the context. This paper proposes a way of identifying and analysing the targets of the opinions and their modifiers, along with their linkage (appraisal group) through an annotation schema called SentiML. Such schema has been developed in order to facilitate the identification of these elements and the annotation of their sentiment, along with advanced linguistic features such as their appraisal type according to the Appraisal Framework. The schema is XML-based and has been also designed to be language-independent. Preliminary results show that the schema allows more coverage than a sentiment dictionary, while achieving reasonably fast and reliable annotation in spite of its fine granularity.

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Cited By

Chander DSingh HGupta A(2022)A Study of Big Data Processing for Sentiments AnalysisResearch Anthology on Big Data Analytics, Architectures, and Applications10.4018/978-1-6684-3662-2.ch056(1162-1191)Online publication date: 2022
https://doi.org/10.4018/978-1-6684-3662-2.ch056
Chander DSingh HGupta A(2021)A Study of Big Data Processing for Sentiments AnalysisLarge-Scale Data Streaming, Processing, and Blockchain Security10.4018/978-1-7998-3444-1.ch001(1-38)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-3444-1.ch001
Roe CLowe MWilliams BMiller C(2021)Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment AnalysisInternational Journal of Environmental Research and Public Health10.3390/ijerph18241302818:24(13028)Online publication date: 10-Dec-2021
https://doi.org/10.3390/ijerph182413028
Show More Cited By

Index Terms

SentiML: functional annotation for multilingual sentiment analysis
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection

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cover image ACM Other conferences

DH-CASE '13: Proceedings of the 1st International Workshop on Collaborative Annotations in Shared Environment: metadata, vocabularies and techniques in the Digital Humanities

September 2013

113 pages

ISBN:9781450321990

DOI:10.1145/2517978

Conference Chairs:
Francesca Tomasi
University of Bologna, Italy
,
Fabio Vitali
University of Bologna, Italy

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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AIUCD: Associazione per l Informatica Umanistica e la Cultura Digitale

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Published: 10 September 2013

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DH-case '13

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DH-case '13: metadata, vocabularies and techniques in the Digital Humanities

September 10, 2013

Florence, Italy

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DH-CASE '13 Paper Acceptance Rate 18 of 30 submissions, 60%;

Overall Acceptance Rate 18 of 30 submissions, 60%

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Cited By

Chander DSingh HGupta A(2022)A Study of Big Data Processing for Sentiments AnalysisResearch Anthology on Big Data Analytics, Architectures, and Applications10.4018/978-1-6684-3662-2.ch056(1162-1191)Online publication date: 2022
https://doi.org/10.4018/978-1-6684-3662-2.ch056
Chander DSingh HGupta A(2021)A Study of Big Data Processing for Sentiments AnalysisLarge-Scale Data Streaming, Processing, and Blockchain Security10.4018/978-1-7998-3444-1.ch001(1-38)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-3444-1.ch001
Roe CLowe MWilliams BMiller C(2021)Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment AnalysisInternational Journal of Environmental Research and Public Health10.3390/ijerph18241302818:24(13028)Online publication date: 10-Dec-2021
https://doi.org/10.3390/ijerph182413028
Mhaske NPatil A(2021)Resource creation for opinion mining: a case study with Marathi movie reviewsInternational Journal of Information Technology10.1007/s41870-021-00698-8Online publication date: 26-May-2021
https://doi.org/10.1007/s41870-021-00698-8
Missen MCoustaty MAsmat HFirdous AAkhtar NAkram MPrasath V(2020)2Es of TISRecommender System with Machine Learning and Artificial Intelligence10.1002/9781119711582.ch3(45-70)Online publication date: 15-Jul-2020
https://doi.org/10.1002/9781119711582.ch3
Attik MMissen MCoustaty MChoi GAlotaibi FAkhtar NJhandir MPrasath VSalamat NHusnain M(2019)OpinionML—Opinion Markup Language for Sentiment RepresentationSymmetry10.3390/sym1104054511:4(545)Online publication date: 15-Apr-2019
https://doi.org/10.3390/sym11040545
Invento ALerias TCeniza A(2017)Sentiment Analysis on the Impact of K-12 Program in the Philippines using Naïve Bayes and Lexicon Approach with Code SwitchingProceedings of the 2017 International Conference on Information Technology10.1145/3176653.3176683(103-106)Online publication date: 27-Dec-2017
https://dl.acm.org/doi/10.1145/3176653.3176683
Soualah-Alila FCoustaty MRempulski NDoucet A(2016)DataTourism: Designing an Architecture to Process Tourism DataInformation and Communication Technologies in Tourism 201610.1007/978-3-319-28231-2_54(751-763)Online publication date: 23-Jan-2016
https://doi.org/10.1007/978-3-319-28231-2_54
Missen MAttik MCoustaty MDoucet AFaucher C(2016)SentiML++: An Extension of the SentiML Sentiment Annotation SchemeThe Semantic Web: ESWC 2015 Satellite Events10.1007/978-3-319-25639-9_18(91-96)Online publication date: 9-Jan-2016
https://doi.org/10.1007/978-3-319-25639-9_18

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