Tag recommendation in social networks with the help of text summarization and KNN

In recent years, the utilization of social networks has surged markedly, with interest in their use escalating daily.A pivotal concern is augmenting the number of views for individuals' posts or messages to enhance their popularity.The most effective means to achieve this objective is through the use of tags.Tags significantly contribute to the organization and retrieval of existing data, and the automatic generation of tags has garnered substantial attention.Tag recommendation from textual sources can be approached as a text extraction issue.This paper endeavors to propose a comprehensive set of suggested 1 Tag 2 Bag Of Words 3 Explicit Semantic Analysis 1403 4 62 16 keywords derived from data via advanced text summarization techniques, culminating in the presentation of a sophisticated tag recommender.Consequently, this research introduces an innovative and robust solution by integrating clustering, summarization, and recommendation methodologies.Initially, utilizing the Bag of Words (BoW) model, comprehensive word parsing and extraction of word roots are performed.This process yields a bag of words capable of facilitating deep semantic exploration.The data is meticulously simplified to its core elements, with prepositions and repetitions omitted.Verbs, due to their high frequency and significance depending on the context of the sentence or post, are mined separately.Other words are judiciously selected based on their frequency and importance, and stored with their repetition counts.Subsequently, employing the K-Nearest Neighbor (KNN) clustering algorithm, the data is clustered, and the cluster representatives serve as the output tags.A slight modification is made to the KNN algorithm by incorporating the Explicit Semantic Analysis (ESA) method for precise scale calculations.The proposed solution was rigorously evaluated on two public datasets: TPA, extracted by Aminer, and AG, extracted by ComeToMyHead.The AG dataset comprises 127,600 news articles, categorized into four distinct tag types.Each category contains 30,000 training samples and 1,900 test samples, with a total of 31,900 tags representing global, sports, business, and scientific concepts.The findings of this study were compared with those from 13 similar research papers, which fall into four distinct categories: machine learning, long-short-term memory (LSTM), convolutional neural network (CNN), and capsule-based models.The comparative analysis revealed that the proposed method demonstrates superior accuracy, comprehensive coverage, and an enhanced F-measure.The integration of advanced text analytics techniques underscores the significance of this study in the broader context of information retrieval and data mining.By harnessing the power of semantic analysis and machine learning, this research provides a novel framework that not only enhances the efficiency of tag recommendation systems but also contributes to the theoretical foundation of automated keyword extraction.The implications of these findings are far-reaching, with potential applications extending beyond social networks to other domains requiring efficient data organization and retrieval.