Evidence map›Paper›PMID 36373074›Full record

ArticleMultimedia tools and applications2023

An opinion mining methodology to analyse games for health.

Paula Alexandra Silva, Renato Santos

Abstract read
In one paragraph

Article in Multimedia tools and applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Paula Alexandra SilvaDepartment of Informatics Engineering, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Coimbra, Portugal.ORCID 0000-0003-1573-7446
Renato SantosDepartment of Informatics Engineering, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Coimbra, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the positive impact of games for health on players' health, users tend to stop playing them after a short period of time, leading benefits to fade. It is therefore important to understand how to sustain interest and, in this way, preserve the health benefits of games for health. This could be achieved by continuously reviewing user feedback after product launch and using this information to inform (re)design and better address user needs. With the growth of social media, user opinions became widely available in public forums. This abundance of information affords us the possibility of, through the application of natural language processing and sentiment analysis techniques, tapping into user opinions and automatically analysing and extracting knowledge from them. This paper introduces a methodology that analyses user comments posted on YouTube about the Just Dance game, to automatically extract information about Usability, User Experience (UX), and Perceived Health Impacts related to Quality of Life (H-QoL). In doing so, the methodology uses a pre-established vocabulary, based on the English lexicon and its semantic relations, to annotate the presence of 38 concepts (five of Usability, 18 of UX, and 15 of H-QoL) and to analyse sentiment. The results of the information extraction and processing are displayed on a dashboard that allows for the exploration and browsing of the results, which can be useful to better understand the opinions and impacts perceived by users and to inform the (re)design of games for health. The methodology proposed builds upon over 500,000 user comments collected from over 32,000 videos.

Indexed as

HealthNatural language processingOpinion miningQuality of lifeUsabilityUser experience

Identifiers

PMID36373074
PMCPMC9638502

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.