Evidence map›Paper›PMID 38820351›Full record

ArticlePloS one2024

Unveiling factors influencing judgment variation in sentiment analysis with natural language processing and statistics.

Olga Kellert, Carlos Gómez-Rodríguez, Mahmud Uz Zaman

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Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Olga KellertCITIC, Grupo LyS, Depto. de Ciencias de la Computación y Tecnologías de la Información, Universidade da Coruña, A Coruña, Spain.ORCID 0000-0001-8601-8305
Carlos Gómez-RodríguezCITIC, Grupo LyS, Depto. de Ciencias de la Computación y Tecnologías de la Información, Universidade da Coruña, A Coruña, Spain.ORCID 0000-0003-0752-8812
Mahmud Uz ZamanThe Applied Computational Linguistics (ACoLi) Lab, University of Augsburg, Augsburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

TripAdvisor reviews and comparable data sources play an important role in many tasks in Natural Language Processing (NLP), providing a data basis for the identification and classification of subjective judgments, such as hotel or restaurant reviews, into positive or negative polarities. This study explores three important factors influencing variation in crowdsourced polarity judgments, focusing on TripAdvisor reviews in Spanish. Three hypotheses are tested: the role of Part Of Speech (POS), the impact of sentiment words such as "tasty", and the influence of neutral words like "ok" on judgment variation. The study's methodology employs one-word titles, demonstrating their efficacy in studying polarity variation of words. Statistical tests on mean equality are performed on word groups of our interest. The results of this study reveal that adjectives in one-word titles tend to result in lower judgment variation compared to other word types or POS. Sentiment words contribute to lower judgment variation as well, emphasizing the significance of sentiment words in research on polarity judgments, and neutral words are associated with higher judgment variation as expected. However, these effects cannot be always reproduced in longer titles, which suggests that longer titles do not represent the best data source for testing the ambiguity of single words due to the influence on word polarity by other words like negation in longer titles. This empirical investigation contributes valuable insights into the factors influencing polarity variation of words, providing a foundation for NLP practitioners that aim to capture and predict polarity judgments in Spanish and for researchers that aim to understand factors influencing judgment variation.

Indexed as

JudgmentNatural Language ProcessingHumansLanguage

Identifiers

PMID38820351
PMCPMC11142521

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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.