Investigating the Consistency of Emoji Sentiment Lexicons Constructed Using Different Languages

Kimura, Mayu; Katsurai, Marie

doi:10.1145/3282373.3282417

Cited by 4 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Emojis are increasingly used to express sentiments in text-based communication. Unlike studies such as [23,24] which aim to construct emoji sentiment lexicons, the Fig. 4: Influence of the threshold θ across all 13 domains.…”

Section: Emojis Lexiconsmentioning

confidence: 99%

SentiLexIT: Advancing Italian Sentiment Analysis through Automated Lexicon Generation

de Melo,

Merialdo

2024

Preprint

View full text Add to dashboard Cite

The proliferation of digital platforms has exponentially increased the volume of user-generated content, providing rich data sources for sentiment analysis. This study introduces SentiLexIT, a novel methodology for automatically constructing domain-specific sentiment lexicons for the Italian language using an unsupervised approach. We analyzed 321,817 user reviews from diverse categories such as Amazon.it and Google Reviews, applying Bayes' Theorem to derive sentiment scores that effectively categorize review sentiments into distinct polarities. Our methodology significantly outperforms existing Italian sentiment lexicons and two versions of ChatGPT, achieving an average F1-score of 0.92 across various domains. The paper highlights the method's effectiveness in handling linguistic nuances specific to Italian, providing a robust tool for sentiment analysis. This research contributes to the field by offering a scalable, precise methodology for sentiment lexicon construction without the need for manual annotation, addressing the gap in non-English sentiment analysis resources.

show abstract

Section: Emojis Lexiconsmentioning

confidence: 99%