CNN-VAE: An intelligent text representation algorithm

Xu, Saijuan; Guo, Canyang; Zhu, Yanyan; Liu, Genggeng; Xiong, Neal N.

doi:10.1007/s11227-023-05139-w

Cited by 4 publications

(1 citation statement)

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“…Generative Adversarial Networks (GAN) have received much attention and produced impressive results. One such model is the (VAE) [30], [31]which is a type of generative model that learns a compressed representation of data, called latent variables, that can be used to generate new data samples…”

Section: Related Workmentioning

confidence: 99%

Expressivity of Tweets on Social Issues Using Aspect Based Text Classification

Thomas,

Parthasarathy

2023

IJRITCC

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Social discussions about crime on Twitter and open forums aims to understand the barriers that hinder people from expressing their concerns or aligning with popular opinions. A curated dataset spanning three months in 2023 was collected, covering categories like crimes and Gender Equality and Violence Against Women. The study employs aspect-based sentiment analysis to classify sentiment polarity in tweets, utilising a comprehensive framework involving three text feature classification stages. The initial stage analyses individual words, phrases, and tweet patterns to classify text features based on specific linguistic elements. In the subsequent step, semantic relations explore a better understanding of the core sentiment and infer relationships between different text keywords. This stage enhances the analysis by considering the meaning and contextual nuances of the language used in the tweets. The final stage incorporates transformer-based models for effective multilabel classification to view the diversity present in the dataset. The study's quantitative analysis reveals that the Ensemble learning approach demonstrates an impressive precision measure of 93%. By integrating the three stages of text feature classification, the study enhances the accuracy and comprehensiveness of sentiment analysis in social discussions about crime on Twitter.

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Section: Related Workmentioning

confidence: 99%