Iterated Greedy

Stützle, Thomas; Ruíz, Rubén

doi:10.1007/978-3-319-07124-4_10

Cited by 11 publications

(1 citation statement)

References 92 publications

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“…[5] opted for the Iterated Greedy [15] algorithm due to its capacity to handle dimensionality challenges by exploring the search space through feature removal/addition during the algorithm's construction/destruction stage. However, similar to the previous research, this study did not address the treatment of emojis or the consideration of negation word reversal in the sentiment classification model [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Development of an Effective Sentiment Classification Architecture for Texts Containing Negation Words and Emojis

Bonga,

Varshney

2023

Preprint

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The widespread use of social media platforms like Facebook, Instagram, Reddit, and Twitter has profoundly influenced human interactions and decision-making. Twitter, in particular, has become a popular platform for sharing opinions on diverse topics, resulting in a substantial volume of user-generated content known as tweets. Emojis, which convey emotions and moods, have become integral to online communication. However, existing sentiment analysis methods struggle with the unique characteristics of tweets, such as negation words, contracted phrases, shortened text, language ambiguities, and emojis. To address these challenges, a novel word representational framework is proposed in this research, considering both negation words and emojis. Sentiment analysis is performed using Long Short-Term Memory (LSTM) neural networks to capture both syntactic and semantic features of tweets, enhancing sentiment classification efficiency. Extensive experiments on three benchmark datasets validate the approach, resulting in impressive accuracy rates of 95.24%, 87.13%, and 94.18% on different datasets. These results underscore the effectiveness of the proposed method in handling the complexities of sentiment analysis in the context of tweets, with applications in business insights, government policy formulation, decision-making processes, and brand identity monitoring.

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Section: Literature Reviewmentioning

confidence: 99%