Wikipedia-based hybrid document representation for textual news classification

García, Marcos Antonio Mouriño; Rodríguez, Ramón López; Ferro, Manuel Vilares; Anido‐Rifón, Luis E.

doi:10.1007/s00500-018-3101-5

Cited by 11 publications

(7 citation statements)

References 39 publications

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“…• It can also be clearly seen that the performance metrics are higher when enriching the tweets representations with the features extracted from the text of the tweets by using Wikipedia Miner, achieving F1-score improvements up to 13% for the Random Forest algorithm and up to 22.23% for the CART algorithm. This is a clear evidence that the knowledge contained in Wikipedia provides very relevant information to the classifier, thus improving its performance, which is in line with what was stated in previous studies [14]- [16]. • Finally, after the analysis of the results presented, we concluded that the best option for this particular case is the CART algorithm, since it shows performance values similar to Random Forests with significantly lower training and classification times.…”

Section: ) Classifier Results and Analysissupporting

confidence: 89%

“…Wikipedia Miner is a general purpose semantic annotator based on natural language processing, machine learning techniques, and the use of Wikipedia as background knowledge. This approach has been successfully applied in previous studies for the classification of, among others, biomedical documents [14], documents of legal nature [15], and news [16]. The main characteristics of Wikipedia Miner are: 1) It identifies concepts that appear in documents, thus avoiding the generation of irrelevant features; 2) it performs word sense disambiguation, thus tackling synonymy and polysemy problems; 3) it links the extracted concepts from documents to Wikipedia entries; and 4) it assigns a weight to each extracted concept according to its relevance in the text.…”

Section: A Document (Tweet) Representationmentioning

confidence: 99%

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Detection of Barriers to Mobility in the Smart City Using Twitter

et al. 2020

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We present a system that analyzes data extracted from the microbloging site Twitter to detect the occurrence of events and obstacles that can affect pedestrian mobility, with a special focus on people with impaired mobility. First, the system extracts tweets that match certain predefined terms. Then, it obtains location information from them by using the location provided by Twitter when available, as well as searching the text of the tweet for locations. Finally, it applies natural language processing techniques to confirm that an actual event that affects mobility is reported and extract its properties (which urban element is affected and how). We also present some empirical results that validate the feasibility of our approach.

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Section: ) Classifier Results and Analysissupporting

confidence: 89%

Section: A Document (Tweet) Representationmentioning

confidence: 99%

Detection of Barriers to Mobility in the Smart City Using Twitter

et al. 2020

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“…Kroha and Baeza-Yates [67] processed a corpus of news published from 1999 to 2002 in order to explore the impact of several factors (term frequency, grammatical structure, and context) on the actual news classification. Furthermore, Mouriño-Garcia, Pérez-Rodríguez, Anido-Rifón, and Vilares-Ferro [68] proposed a hybrid approach that enriches the traditional BoW representation with background knowledge for the semantic analysis of texts. The results indicated that their concepts-based approach adds information that improves classification performance for news items.…”

Section: A Brief Review Of the Related Literaturementioning

confidence: 99%

News Classification for Identifying Traffic Incident Points in a Spanish-Speaking Country: A Real-World Case Study of Class Imbalance Learning

et al. 2020

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‘El Diario de Juárez’ is a local newspaper in a city of 1.5 million Spanish-speaking inhabitants that publishes texts of which citizens read them on both a website and an RSS (Really Simple Syndication) service. This research applies natural-language-processing and machine-learning algorithms to the news provided by the RSS service in order to classify them based on whether they are about a traffic incident or not, with the final intention of notifying citizens where such accidents occur. The classification process explores the bag-of-words technique with five learners (Classification and Regression Tree (CART), Naïve Bayes, kNN, Random Forest, and Support Vector Machine (SVM)) on a class-imbalanced benchmark; this challenging issue is dealt with via five sampling algorithms: synthetic minority oversampling technique (SMOTE), borderline SMOTE, adaptive synthetic sampling, random oversampling, and random undersampling. Consequently, our final classifier reaches a sensitivity of 0.86 and an area under the precision-recall curve of 0.86, which is an acceptable performance when considering the complexity of analyzing unstructured texts in Spanish.

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“…In addition, online hot news prediction has important application value; firstly, it can enable the government to grasp the trend of public opinion in a timely manner, which is convenient for the government to manage public opinion and grasp and handle sudden public events; secondly, it can help news websites manage the release locations of different news, Put hot news in the area that users pay more attention to, thereby increasing the influence of news websites; at the same time, it promotes the public to pay attention to the current hot news in a timely manner, and triggers thinking about daily life from the news, thereby improving the quality of life. For example, when hot news related to telecommunication fraud occupies the homepage of major fine-textured websites, it can increase people's attention to and beware of telecommunication fraud, and help people learn the relevant knowledge of preventing telecommunication fraud [9][10][11][12]. Network news has become the main source of network waves and public opinion, and it is of great theoretical and applied value to accurately predict hot news and attract public attention and discussion [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Prediction of News Popularity Based on Deep Neural Network

Cai

Zhang²

2022

Scientific Programming

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Due to the timeliness and short life cycle of news, the postrelease prediction is limited, and the prerelease prediction also faces huge challenges due to the diversity and difficulty of defining influencing factors. This paper uses the foreclosure. This paper proposes a news popularity prediction method based on GRU deep neural network. Firstly, a web crawler was designed to obtain news data of different types and structures from 10 information security portal websites in China. After data preprocessing, the Word2Vec method was used to extract features, extract key news sentences and construct a subset of content features. Establish a GRU neural network regression prediction model to predict hot news on the Internet. The experimental results show that, compared with the traditional processing method, the model can process the multi-source rough data set in this paper and greatly reduce the prediction error. At the same time, because the threshold recurrent unit structure used in this paper is simpler than the long-short-term memory network structure, it can shorten the prediction time and improve the computing performance.

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Wikipedia-based hybrid document representation for textual news classification

Cited by 11 publications

References 39 publications

Detection of Barriers to Mobility in the Smart City Using Twitter

Detection of Barriers to Mobility in the Smart City Using Twitter

News Classification for Identifying Traffic Incident Points in a Spanish-Speaking Country: A Real-World Case Study of Class Imbalance Learning

Prediction of News Popularity Based on Deep Neural Network

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