Emotion aware feature based opining mining on large scale data by exploring hypergraph with helly property

Pradeepa, S.; Sasikaladevi, N.; Manjula, K.

doi:10.1007/s11042-021-11311-2

Cited by 2 publications

(1 citation statement)

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“…Experiments were run using the Naive Bayes, FLR, and AdaBoost classifiers, and the results were associated to assess the feature selection strategies. [13] intended that users' featurebased online comments have become more important to both customers making purchases and companies searching for feedback on their products on e-commerce platforms. Therefore, it's vital to develop opinion-mining platforms centred on eliciting customers' feature-based assessments of items.…”

Section: IImentioning

confidence: 99%

Feature-Based Opinion Classification Using the KPCA Technique: Concept and Performance Evaluation

Kumar¹,

Ahuja²

2022

IJRITCC

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Over the last several years, a widespread trend on the internet has been the proliferation of online evaluations written by people with whom they share their ideas, interests, experiences, and opinions. Opinion mining, also known as sentiment analysis, is the process of classifying pieces of text written in a natural language on a subject into positive, negative, or neutral categories according to the human emotions, views, and feelings that are communicated in that text. The field of sentiment analysis has progressed to the point that it can now analyse internet evaluations and provide significant information to people as well as corporations, which may assist these parties in the decision-making process. In the proposed model, feature extraction extracts the collection of features that are both semantically and statistically significant using the kernel principal component analysis (KPCA) method. According to the findings of the simulations, the suggested model performs better than other existing models.

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Section: IImentioning

confidence: 99%