SigmaLaw PBSA - A Deep Learning Approach For Aspect Based Sentiment Analysis in Legal Opinion Texts

Rajapaksha, Isanka; Mudalige, Chanika Ruchini; Karunarathna, Dilini; Silva, Nisansa de; Ratnayaka, Gathika; Perera, and Amal Shehan

doi:10.26421/jdi3.1-1

Cited by 4 publications

(1 citation statement)

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“…Future work will involve adapting the model to various distributed deep learning applications. [5] This work discusses BILEAT, a very robust and generalised method for unified aspect-based sentiment analysis.…”

Section: Literature Surveymentioning

confidence: 99%

Survey on Aspect Based Sentimental Analysis using Deep Learning Techniques

Nandini¹,

Priya²,

Sruthi³

et al. 2023

Int. J. Res. Publ. Rev.

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People People are increasingly using social media to communicate their views and ideas in daily life. Analysis, processing, interpretation, and inference of subjective texts with the sentiment are all steps in the sentiment analysis process. Businesses utilise sentiment analysis for a variety of purposes, including market research, brand reputation analysis, customer experience evaluation, and social media influence research. It can be classified into document, sentence, and aspect-based types in accordance with the various needs for aspect granularity. In order to determine the aspects of entities and the sentiment conveyed for each one, Aspect Based Sentimental Analysis (ABSA), a fine-grained task in sentiment analysis, is performed. Since it offers more thorough and detailed data, ABSA has drawn a lot of interest in recent years. Product dependence, target dependence, and aspect dependence are the three fundamental difficulties ABSA faces. In order to choose the best aspect extraction approach for the proposed model, we first compared the accuracy of two different methods, TF-IDF and PMI. The precise extraction technique is examined first with Bert and subsequently with various classifiers. The many classifiers are finally combined to produce a superior accuracy. Pretrained BERT that has been fine-tuned performs very well on ABSA. It makes use of BERT intermediate layers to improve the effectiveness of BERT finetuning. The suggested approach will be tested using two datasets, Amazon Unlocked Mobile and tripadvisor hotel reviews, where hotel reviews produce more accurate findings.

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Section: Literature Surveymentioning

confidence: 99%