Legal Judgment Prediction in the Context of Energy Market using Gradient Boosting

França, João Vitor Ferreira; Boaro, Jose M. C.; Santos, Pedro; Henrique, Fernando; Garcia, Venicius; Manfredini, Caio; Domingos, A. D.; Oliveira, Francisco Y. C. de; Castro, Carlos E. P.; Bráz, Geraldo; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de; Oliveira, Milton S. L. de; Moraes, Renato U. Moreira e; Alves, Erika W. B. A. L.; Neto, Jose S. Sobral

doi:10.1109/smc42975.2020.9283297

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…There exists different oversampling and under-sampling methods to balance the dataset. In oversampling different methods are used to balance the skewed dataset i.e., random oversampling, ADASYN (Adaptive Synthetic Sampling), smoting, borderline smooting, smoot-NC, Kmean smooting, and SVM smmoting etc [54][55][56]. For the under-sampling method to balanced the dataset there exist different techniques i.e., Random under-sampling for the majority class, NearMiss, Condensed Nearest Neighbor Rule, TomekLinks, Edited Nearest Neighbor Rule and Cluster Centroids etc.…”

Section: Imbalanced Class Handlingmentioning

confidence: 99%

Aspect Level Songs Rating Based Upon Reviews in English

Qureshi¹,

Asif²,

Anwar³

et al. 2023

Computers, Materials &Amp; Continua

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With the advancements in internet facilities, people are more inclined towards the use of online services. The service providers shelve their items for e-users. These users post their feedbacks, reviews, ratings, etc. after the use of the item. The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items. Sentiment Analysis (SA) is a technique that performs such decision analysis. This research targets the ranking and rating through sentiment analysis of these reviews, on different aspects. As a case study, Songs are opted to design and test the decision model. Different aspects of songs namely music, lyrics, song, voice and video are picked. For the reason, reviews of 20 songs are scraped from YouTube, pre-processed and formed a dataset. Different machine learning algorithms-Naïve Bayes (NB), Gradient Boost Tree, Logistic Regression LR, K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN) are applied. ANN performed the best with 74.99% accuracy. Results are validated using K-Fold.

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Section: Imbalanced Class Handlingmentioning

confidence: 99%