Recommendation System Based on Generalized-Weighted Tree Similarity Algorithm

Krishna, D. Pramodh; Rao, K. Venu Gopal

doi:10.1007/978-981-10-7868-2_15

Cited by 1 publication

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of the works have been done in the area of opinion examination by using ML approaches [16]. The efficiency of proposed method is examined with existing approaches like ABCDM [17], NEA [18], and SLA [19].Recommendation systems were developed for tree structure data using tree similarity algorithms [20], [21].…”

Section: Introductionmentioning

confidence: 99%

A Novel Elephant Herd Random Forest Machine Learning Approach To Estimate the Sentiment Value of Online Customer Review

Sanshi¹,

Krishna²,

Vatambeti³

2023

IJCDS

View full text Add to dashboard Cite

Nowadays, big data has attracted the attention of whole advanced world with its applications and features. Machine learning (ML) models are used to perform the online administrations in good manner .The ML approaches turned into a moving field in analyzing enormous data; consequently the accomplishment of online administrations or business depends on the client audits. Nearly the online customer review contains positive, negative and neutral sentiment value. In marketing system and product development fields the sentiment analysis value prediction has important role. In this paper, a novel Elephant Herd Random Forest Machine Learning (EHRFML) methodology is proposed to compute the sentiment value of online customer review. Moreover, customer review datasets are preprocessed and unwanted information is removed using machine learning approach. Sequentially, the outcomes of proposed system are compared with existing technique using parameters like accuracy, precision, recall, aspect term specification and opinion condition obtained good results by getting high accuracy based on opinion specification.

show abstract