Literature DB >> 27193159

Sentiment Analysis and Social Cognition Engine (SEANCE): An automatic tool for sentiment, social cognition, and social-order analysis.

Scott A Crossley1, Kristopher Kyle2, Danielle S McNamara3.   

Abstract

This study introduces the Sentiment Analysis and Cognition Engine (SEANCE), a freely available text analysis tool that is easy to use, works on most operating systems (Windows, Mac, Linux), is housed on a user's hard drive (as compared to being accessed via an Internet interface), allows for batch processing of text files, includes negation and part-of-speech (POS) features, and reports on thousands of lexical categories and 20 component scores related to sentiment, social cognition, and social order. In the study, we validated SEANCE by investigating whether its indices and related component scores can be used to classify positive and negative reviews in two well-known sentiment analysis test corpora. We contrasted the results of SEANCE with those from Linguistic Inquiry and Word Count (LIWC), a similar tool that is popular in sentiment analysis, but is pay-to-use and does not include negation or POS features. The results demonstrated that both the SEANCE indices and component scores outperformed LIWC on the categorization tasks.

Entities:  

Keywords:  Affect detection; Automatic tools; Corpus linguistics; Natural language processing; Opinion mining; Sentiment analysis

Mesh:

Year:  2017        PMID: 27193159     DOI: 10.3758/s13428-016-0743-z

Source DB:  PubMed          Journal:  Behav Res Methods        ISSN: 1554-351X


  10 in total

1.  Emotionally evocative patients in the emergency department: a mixed methods investigation of providers' reported emotions and implications for patient safety.

Authors:  Linda M Isbell; Julia Tager; Kendall Beals; Guanyu Liu
Journal:  BMJ Qual Saf       Date:  2020-01-27       Impact factor: 7.035

2.  A large-scaled corpus for assessing text readability.

Authors:  Scott Crossley; Aron Heintz; Joon Suh Choi; Jordan Batchelor; Mehrnoush Karimi; Agnes Malatinszky
Journal:  Behav Res Methods       Date:  2022-03-16

3.  Diabetes Self-Management in the Age of Social Media: Large-Scale Analysis of Peer Interactions Using Semiautomated Methods.

Authors:  Sahiti Myneni; Brittney Lewis; Tavleen Singh; Kristi Paiva; Seon Min Kim; Adrian V Cebula; Gloria Villanueva; Jing Wang
Journal:  JMIR Med Inform       Date:  2020-06-30

4.  Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study.

Authors:  Renu Balyan; Scott A Crossley; William Brown; Andrew J Karter; Danielle S McNamara; Jennifer Y Liu; Courtney R Lyles; Dean Schillinger
Journal:  PLoS One       Date:  2019-02-22       Impact factor: 3.240

5.  Automated content analysis across six languages.

Authors:  Leah Cathryn Windsor; James Grayson Cupit; Alistair James Windsor
Journal:  PLoS One       Date:  2019-11-20       Impact factor: 3.240

6.  Precision communication: Physicians' linguistic adaptation to patients' health literacy.

Authors:  Dean Schillinger; Nicholas D Duran; Danielle S McNamara; Scott A Crossley; Renu Balyan; Andrew J Karter
Journal:  Sci Adv       Date:  2021-12-17       Impact factor: 14.136

7.  Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models.

Authors:  Maxwell Levis; Christine Leonard Westgate; Jiang Gui; Bradley V Watts; Brian Shiner
Journal:  Psychol Med       Date:  2020-02-17       Impact factor: 7.723

8.  Valence and arousal ratings for 11,310 simplified Chinese words.

Authors:  Xu Xu; Jiayin Li; Huilin Chen
Journal:  Behav Res Methods       Date:  2021-06-07

9.  Predicting the readability of physicians' secure messages to improve health communication using novel linguistic features: Findings from the ECLIPPSE study.

Authors:  Scott A Crossley; Renu Balyan; Jennifer Liu; Andrew J Karter; Danielle McNamara; Dean Schillinger
Journal:  J Commun Healthc       Date:  2020-09-24

10.  Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study.

Authors:  Dean Schillinger; Renu Balyan; Scott A Crossley; Danielle S McNamara; Jennifer Y Liu; Andrew J Karter
Journal:  Health Serv Res       Date:  2020-09-23       Impact factor: 3.734

  10 in total

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