Sentimental Analysis based on hybrid approach of Latent Dirichlet Allocation and Machine Learning for Large-Scale of Imbalanced Twitter Data

Jamal, Nasir; Chen, Xianqiao; Abro, Junaid Hussain; Tukhtakhunov, Doniyor

doi:10.1145/3446132.3446413

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…In this section, the topic distribution is accomplished using the Latent Dirichlet Allocation (LDA) technique for quantitatively analyzing the topics in the generated dataset [30] . The LDA technique is an effective topic model, which captures the topics from the weighted features and then each tweet is classified based on the concepts.…”

Section: Methodsmentioning

confidence: 99%

Twitter sentiment analysis using ensemble based deep learning model towards COVID-19 in India and European countries

Sunitha¹,

Patra²,

Babu³

et al. 2022

Pattern Recognition Letters

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Section: Methodsmentioning

confidence: 99%

Twitter sentiment analysis using ensemble based deep learning model towards COVID-19 in India and European countries

Sunitha¹,

Patra²,

Babu³

et al. 2022

Pattern Recognition Letters

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“…The deep learning-based approaches [1] have proved their importance in natural language processing tasks including emotion detection from text. This paper presents an experiment with a combination of Word2Vec embedding and convolution neural network (CNN) model that outperforms the traditional machine learning approach like random forest (RF) and logistic regression (LR) with term frequency-inverse document frequency (TF-IDF) methods [2] and deep learning approaches in the literature. Word2Vec captures the semantic and contextual relationship of a given text which helps in getting a more fine-tuned emotion detection model.…”

Section: Introductionmentioning

confidence: 99%

Emotion detection using Word2Vec and convolution neural networks

Jadon,

Kumar

2024

IJEECS

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<div>Emotion detection from text plays a very critical role in different domains, including customer service, social media analysis, healthcare, financial services, education, human-to-computer interaction, psychology, and many more. Nowadays, deep learning techniques become popular due to their capabilities to capture inherent complex insights and patterns from raw data. In this paper, we have used the Word2Vec embedding approach that takes care of the semantic and contextual understanding of text making it more realistic while detecting emotions. These embeddings act as input to the convolution neural network (CNN) to capture insights using feature maps. The Word2Vec and CNN models applied to the international survey on emotion antecedents and reactions (ISEAR) dataset outperform the models in the literature in terms of accuracy and F1-score as model evaluation metrics. The proposed approach not only obtains high accuracy in emotion detection tasks but also generates interpretable representations that contribute to the understanding of emotions in textual data. These findings carry significant implications for applications in diverse domains, such as social media analysis, market research, clinical assessment and counseling, and tailored recommendation systems.</div>

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