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Published on: January 29, 2020
Unveiling factors influencing judgment variation in sentiment analysis with natural language processing and
Olga Kellert1, Carlos Gómez-Rodríguez1, Mahmud Uz Zaman2
1CITIC, Grupo LyS, Depto. de Ciencias de la Computación y Tecnologías de la Información, Universidade da Coruña, A Coruña, Spain.
This study on Spanish TripAdvisor reviews found that adjectives and sentiment words in single-word titles reduce polarity judgment variation. Neutral words, however, increase variation, with effects diminishing in longer titles.
Area of Science:
- Natural Language Processing (NLP)
- Computational Linguistics
- Sentiment Analysis
Background:
- Crowdsourced data, like TripAdvisor reviews, is crucial for NLP tasks, especially sentiment analysis.
- Understanding factors influencing polarity judgments in Spanish reviews is essential for accurate NLP applications.
Purpose of the Study:
- To investigate factors affecting polarity judgment variation in Spanish TripAdvisor reviews.
- To test the influence of Part Of Speech (POS), sentiment words, and neutral words on judgment variation.
Main Methods:
- Analysis of one-word titles from Spanish TripAdvisor reviews.
- Statistical tests on mean equality for different word groups (POS, sentiment, neutral).
- Comparison of results from one-word titles versus longer titles.
Main Results:
- Adjectives and sentiment words in one-word titles significantly reduce polarity judgment variation.
- Neutral words are associated with higher judgment variation.
- Observed effects are less consistent in longer titles due to contextual influences.
Conclusions:
- One-word titles are effective for studying isolated word polarity, while longer titles introduce confounding factors.
- Findings provide insights for NLP practitioners and researchers working with Spanish sentiment analysis.
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