department

Free access

Our sentiments, exactly

Author:

Alex WrightAuthors Info & Claims

Communications of the ACM, Volume 52, Issue 4

Pages 14 - 15

https://doi.org/10.1145/1498765.1498772

Published: 01 April 2009 Publication History

All formats PDF

Abstract

With sentiment analysis algorithms, companies can identify and assess the wide variety of opinions found online and create computational models of human opinion.

Cited By

View all

Pattusamy MKanth L(2023)Classification of Tweets Into Facts and Opinions Using Recurrent Neural NetworksInternational Journal of Technology and Human Interaction10.4018/IJTHI.31935819:1(1-14)Online publication date: 10-Mar-2023
https://dl.acm.org/doi/10.4018/IJTHI.319358
Mishra DPanda R(2022)Decoding customer experiences in rail transport service: application of hybrid sentiment analysisPublic Transport10.1007/s12469-021-00289-715:1(31-60)Online publication date: 22-Feb-2022
https://doi.org/10.1007/s12469-021-00289-7
TOKCAER S(2021)Türkçe Metinlerde Duygu AnaliziJournal of Yaşar University10.19168/jyasar.92884316:63(1516-1536)Online publication date: 30-Sep-2021
https://doi.org/10.19168/jyasar.928843
Show More Cited By

Index Terms

Our sentiments, exactly
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

Recommendations

Does sentiment help requirement engineering: exploring sentiments in user comments to discover informative comments
Abstract
User comments are valuable resources for software improvement; however, owing to excessive volume, informative comments need to be selectively analyzed. We attempt to address this problem by sentiment analysis and expect sentiment can be a useful ...
Jointly Discovering Fine-grained and Coarse-grained Sentiments via Topic Modeling
MM '14: Proceedings of the 22nd ACM international conference on Multimedia

The ever-increasing user-generated contents in social media and other web services make it highly desirable to discover opinions of users on all kinds of topics. Motivated by the assumption that individual word and paragraph in documents will deliver ...
Analyzing Sentiments in One Go: A Supervised Joint Topic Modeling Approach

In this work, we focus on modeling user-generated review and overall rating pairs, and aim to identify semantic aspects and aspect-level sentiments from review data as well as to predict overall sentiments of reviews. We propose a novel probabilistic ...

Comments

Information & Contributors

Information

Published In

Communications of the ACM Volume 52, Issue 4

A Direct Path to Dependable Software

April 2009

134 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/1498765

Issue’s Table of Contents

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 April 2009

Published in CACM Volume 52, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Department
Popular
Un-reviewed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
4,657
Total Downloads

Downloads (Last 12 months)99
Downloads (Last 6 weeks)4

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Pattusamy MKanth L(2023)Classification of Tweets Into Facts and Opinions Using Recurrent Neural NetworksInternational Journal of Technology and Human Interaction10.4018/IJTHI.31935819:1(1-14)Online publication date: 10-Mar-2023
https://dl.acm.org/doi/10.4018/IJTHI.319358
Mishra DPanda R(2022)Decoding customer experiences in rail transport service: application of hybrid sentiment analysisPublic Transport10.1007/s12469-021-00289-715:1(31-60)Online publication date: 22-Feb-2022
https://doi.org/10.1007/s12469-021-00289-7
TOKCAER S(2021)Türkçe Metinlerde Duygu AnaliziJournal of Yaşar University10.19168/jyasar.92884316:63(1516-1536)Online publication date: 30-Sep-2021
https://doi.org/10.19168/jyasar.928843
Platzer E(2018)Die Herausforderungen der automatisierten Textanalyse im Wandel der ZeitInformationswissenschaft10.7767/boehlau.9783205791935.291(291-302)Online publication date: 4-Jul-2018
https://doi.org/10.7767/boehlau.9783205791935.291
Chatterjee SDeng SLiu JShan RJiao W(2018)Classifying facts and opinions in Twitter messages: a deep learning-based approachJournal of Business Analytics10.1080/2573234X.2018.15066871:1(29-39)Online publication date: 23-Aug-2018
https://doi.org/10.1080/2573234X.2018.1506687
Becker FBothe SHadwich K(2017)Kundenbindung durch KundenbegeisterungHandbuch Kundenbindungsmanagement10.1007/978-3-658-13650-5_11(273-298)Online publication date: 19-Feb-2017
https://doi.org/10.1007/978-3-658-13650-5_11
Fondevila Gascón JMir Bernal PSantana López ERom Rodríguez J(2016)New Digital Metrics in Marketing: A Comparative Study on Social Media UseMedia and Metamedia Management10.1007/978-3-319-46068-0_45(343-350)Online publication date: 24-Nov-2016
https://doi.org/10.1007/978-3-319-46068-0_45
Chern CWei CShen FFan Y(2015)A sales forecasting model for consumer products based on the influence of online word-of-mouthInformation Systems and e-Business Management10.1007/s10257-014-0265-013:3(445-473)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1007/s10257-014-0265-0
Tian NXu YLi YAbdel-Hafez AJosang A(2015)Generating Product Feature Hierarchy from Product ReviewsWeb Information Systems and Technologies10.1007/978-3-319-27030-2_17(264-278)Online publication date: 16-Dec-2015
https://doi.org/10.1007/978-3-319-27030-2_17
Misopoulos FMitic MKapoulas AKarapiperis C(2014)Uncovering customer service experiences with Twitter: the case of airline industryManagement Decision10.1108/MD-03-2012-023552:4(705-723)Online publication date: 13-May-2014
https://doi.org/10.1108/MD-03-2012-0235
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Digital Edition

View this article in digital edition.

Digital Edition

Magazine Site

View this article on the magazine site (external)

Magazine Site

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

Cited By

Index Terms

Recommendations

Does sentiment help requirement engineering: exploring sentiments in user comments to discover informative comments

Jointly Discovering Fine-grained and Coarse-grained Sentiments via Topic Modeling

Analyzing Sentiments in One Go: A Supervised Joint Topic Modeling Approach

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Digital Edition

Magazine Site

Login options

Full Access

Share

Share this Publication link

Share on social media

Affiliations