# Linked Data Triples Enhance Document Relevance Classification
## 1 Introduction  
Pre-classification of news-feed articles based on user feedback can play an important role in preventing users being overwhelmed with information. In the context of social media, friend importance and textual features are utilized [1] to classify news-feed items on Facebook achieving 64% accuracy using a Support Vector Machine (SVM) classifer. In addition, on Facebook, work has been conducted on classification of feeds on user walls to distinguish between friend posts and liked pages [2], with further sentiment analysis on life-event-related friend posts. A similar document classification and sentiment analysis of news-feeds is conducted in Reference [3] using Bayesian and SVM classifiers. More recently, classification of news articles has been approached using both dictionary-based and machine learning-based techniques on features extracted from sentences represented as text segments (rather than full text) of news articles [4]. Robuzz [5], a New York-based start-up specializing in smart news-feeds, makes use of deep learning techniques and textual features of news content to provide users with a relevant news-feed. While previous work on classification of news-feeds and articles has been mostly around the utilization of bag-of-words and other flat textual features, in this work, we focus on the adaptive analysis of news-feed items from a semantic web perspective, namely using structural elements developed for the semantic web as additional features. Our techniques facilitate the development of a focused crawler that predicts the relevance of documents arriving to a news-feed using several lexical semantic features extracted from the contentof documents already accumulated. For this purpose, we extract and utilize the major Named Entities (NEs) in the news articles--persons, locations, organizations, products, etc.--model and find abstract \"topics\" within each article, and also represent and encode three-way lexical relationships in chunks of texts in the form of subject-predicate-object, referred to as triples or linked data ([[https://en.wikipedia.org/wiki/Linked_data](https://en.wikipedia.org/wiki/Linked_data)]([https://en.wikipedia.org/wiki/Linked_data](https://en.wikipedia.org/wiki/Linked_data)) (accessed on 21 January 2021)). There is evidence in the prior literature that topics, i.e., extracted using the Latent Dirichlet Allocation (LDA) technique [6]--a type of statistical modeling for discovering the abstract topics--in combination with Named Entities (NEs) are features that can semantically enrich documents [7]. Thus, adding LDA topics and NEs is hypothesized to have a positive effect on the performance of a news-feed relevance classifier. On the other hand, the structural features of text, namely the triples, constructed based on the linked data model developed for the semantic web, can capture and encapsulate semantically related chunks of text within each news article. We hypothesize that the utilization of linked data triples that contain LDA topics and NEs and will show that these features provide additional latent semantic information to the task of textual relevance classification in news-feeds. Thus, the contributions of this research are as follows: (i) we confirm the results obtained by [PERSON] et al. [7], namely that LDA topics and NEs, when added as additional features to a corpus, semantically enrich the corpus. This affirms what we refer to as the _topic-entity model_, a model in which LDA topics and named entities complement the original document corpus; (ii) we further show that additional structural features, linked data triples formed by LDA topics and NEs at the sentence level, have an even greater positive latent semantic effect on news-feed classification, we call this the _topic-entity-triple_ model. The contributions of this work are as follows:  
_A binary relevance classification dataset_: named Energy-Hub, containing news articles from various publishers collected through Google NEWS API.