Evidence map›Paper›PMID 36405368›Full record

ArticleMethodsX2022

Analyzing tourism reviews using an LDA topic-based sentiment analysis approach.

Twil Ali, Bencharef Omar, Kaloun Soulaimane

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in MethodsX, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

3 authors.

Twil AliLaboratory of Computer and Systems Engineering, Faculty of Sciences and Techniques, Cadi Ayyad University, Marrakesh 40000, Morocco.
Bencharef OmarLaboratory of Computer and Systems Engineering, Faculty of Sciences and Techniques, Cadi Ayyad University, Marrakesh 40000, Morocco.
Kaloun SoulaimaneLaboratory of Computer and Systems Engineering, Faculty of Sciences and Techniques, Cadi Ayyad University, Marrakesh 40000, Morocco.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It has become increasingly necessary to automate systems for organizing and classifying user reviews according to their domain-specific aspects and sentiment polarities, as online customer opinions have increased on specialized platforms and social networks. This study's methodology employs a combination of topic modeling and sentiment analysis, as well as human validation techniques of topic labels, to extract valuable insights about Marrakech city from TripAdvisor reviews. Through this technique, tourism practitioners and field specialists may dive deeper into online users generated content, leveraging aspect-based sentiment analysis to explore each destination's weaknesses and strengths.•Data collection and pre-processing.•Extracting latent topics using LDA algorithm (Latent Dirichlet Allocation) on collected reviews.•Applying sentiment analysis to each topic.

Indexed as

LDANatural language processingOnline reviewsSentiment analysisTopic modelingUser generated content

Identifiers

PMID36405368
PMCPMC9672446

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.