An Approach for Textual Based Clustering Using Word Embedding

Terra, Ehab; Mohammed, Ammar; Hefny, Hesham A.

doi:10.1007/978-3-030-59338-4_14

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…Other important measures to evaluate the quality of the clusters is the silhouette coef icient. It has performance measure in the interval [−1, 1], and values near zero, indicate overlapping clusters [33]. The dataset using the silhouette coef icient (with cosine similarity) was -0.017.…”

Section: Datasets and Continual Learning Scenariomentioning

confidence: 99%

A Model of Continual and Deep Learning for Aspect Based in Sentiment Analysis

López Ramos,

Artigas Fuentes

2023

JAMRIS

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Sentiment Analysis is a useful tool in several social and business contexts. Aspect Sentiment Classification is a subtask in Sentiment Analysis that gives information about features or aspects of people, entities, products, or services present in reviews. Different Deep Learning models have been proposed to solve Aspect Sentiment Classification focus on a specific domain such as restaurant,hotel, or laptop reviews. However, there are few proposals for creating a single model with high performance in multiple domains. The Continual Learning approach with neural networks has been used to solve aspect classification in multiple domains. However, avoid low aspect classification performance in Continual Learning is challenging. As a consequence, potential neural networkweight shifts in the learning process in different domains or datasets.In this paper, a novel Aspect Sentiment Classification approach is proposed. Our approach combines a Transformer Deep Learning technique with a Continual Learning algorithm in different domains. The input layer used is the pre‐trained model Bidirectional Encoder Representations from Transformers. The experiments show the efficacy of our proposal with 78 .

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Section: Datasets and Continual Learning Scenariomentioning

confidence: 99%