Unveiling factors influencing judgment variation in Sentiment Analysis with Natural Language Processing and Statistics
Olga Kellert, Carlos G\'omez-Rodr\'iguez, Mahmud Uz Zaman

TL;DR
This paper investigates how factors like part of speech, sentiment words, and neutral words influence judgment variation in Spanish sentiment analysis of TripAdvisor reviews, revealing that adjectives and sentiment words reduce variation, while neutral words increase it.
Contribution
It provides empirical insights into the influence of specific linguistic factors on polarity judgment variation, especially highlighting the limitations of longer titles for studying single-word ambiguity.
Findings
Adjectives in one-word titles lower judgment variation.
Sentiment words contribute to more consistent polarity judgments.
Neutral words are associated with higher judgment variation.
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…
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Taxonomy
TopicsImpact of AI and Big Data on Business and Society · Computational and Text Analysis Methods · Technology and Data Analysis
