Polarity enriched attention network for aspect-based sentiment analysis

Wadawadagi, Ramesh S.; Pagi, Veerappa B.

doi:10.1007/s41870-022-01089-3

Cited by 5 publications

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Soni and Mathur [3] have proposed an LSTM with an encoder attention mechanism for sentiment analysis here they used attention mechanisms to extract the aspect features and then go through LSTM for sentiment classification. Wadawadagi and Pagi [4] have presented a novel method that includes Bidirectional LSTM with conditional random field(CRF) and a Polarity Enriched Attention Neural (PEAN) model to extract aspect terms and categorization of sentiments. A novel approach for quantifying sentiment-orientated classification has been proposed by Gurunathan [5].…”

Section: Related Workmentioning

confidence: 99%

Aspect-Based Sentimental Analysis On Social Media Data Using Deep Learning Methods

Uthirapathy,

Sandanam

2024

Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security,

View full text Add to dashboard Cite

Nowadays, reviews related to the products given by users and experts in social media are significant references to help product manufacturers make better decisions about prompting items and placing them in the real world. So analysing aspects such as productrelated sentiments is an important task. The target of aspect-based opinion analysis is to determine the emotional valence of each aspect phrase included inside a given sentence. However, the earlier models make the error of identifying irrelevant contextual terms as cues for determining aspect sentiment. They also overlook syntactical limitations and longrange sentiment dependencies. Hence, we assumed a model using LSTM with GCN to distinguish engagers' review opinions under various aspects with three different labels: negative, neutral, and positive. The proposed model LSTM+GCN shows better performance than other models in terms of 4% with precision, 4% with recall, and 5% with accuracy.

show abstract

Section: Related Workmentioning

confidence: 99%

Aspect-Based Sentimental Analysis On Social Media Data Using Deep Learning Methods

Uthirapathy,

Sandanam

2024

Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security,

View full text Add to dashboard Cite

show abstract

“…Several currently available strategies are reviewed in depth in this section. Wadawadagi et al presented for ABSA a method including the extraction and classification of aspect phrases [20]. The suggested model extracts aspect terms, while the novel Polarity Enriched Attention Neural (PEAN) model is used to classify sentiments.…”

Section: Literature Reviewmentioning

confidence: 99%

Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

Rana¹,

Rehman²,

Nawaz³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events, public products and the latest affairs. People share their thoughts and feelings about various topics, including products, news, blogs, etc. In user reviews and tweets, sentiment analysis is used to discover opinions and feelings. Sentiment polarity is a term used to describe how sentiment is represented. Positive, neutral and negative are all examples of it. This area is still in its infancy and needs several critical upgrades. Slang and hidden emotions can detract from the accuracy of traditional techniques. Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories. Some existing strategies are domain-specific. The proposed model incorporates aspect extraction, association rule mining and the deep learning technique Bidirectional Encoder Representations from Transformers (BERT). Aspects are extracted using Part of Speech Tagger and association rule mining is used to associate aspects with opinion words. Later, classification was performed using BER. The proposed approach attained an average of 89.45% accuracy, 88.45% precision and 85.98% recall on different datasets of products and Twitter. The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

show abstract

An improved sentiment classification model based on data quality and word embeddings

et al. 2023

View full text Add to dashboard Cite

Polarity enriched attention network for aspect-based sentiment analysis

Cited by 5 publications

References 44 publications

Aspect-Based Sentimental Analysis On Social Media Data Using Deep Learning Methods

Aspect-Based Sentimental Analysis On Social Media Data Using Deep Learning Methods

Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

An improved sentiment classification model based on data quality and word embeddings

Contact Info

Product

Resources

About