Hot Topic Detection Based on a Refined TF-IDF Algorithm

Zhu, Zhiliang; Liang, Jie; Li, Deyang; Yu, Hai; Liu, Guoqi

doi:10.1109/access.2019.2893980

Cited by 77 publications

(40 citation statements)

References 16 publications

(22 reference statements)

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“…Finally, the news is clustered based on the representation and events are obtained. The method proposed in this paper solves the problems of a too large data dimension and low computational efficiency of traditional TF-IDF [43], as well as the problems of manual data annotation, which are an inability to identify new events that occurs when using the LDA method [33].…”

Section: Discussionmentioning

confidence: 99%

Food Safety Event Detection Based on Multi-Feature Fusion

et al. 2019

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Food safety event detection is a technique used to discover food safety events by monitoring online news. In general, a set of keywords are extracted as features to represent news, and then the news is clustered to generate events. The most popular method for news feature extraction is Term Frequency-Inverse Document Frequency (TF-IDF), however, it has some defects such as being prone to the “dimension disaster”, low computational efficiency, and a lack of semantic information. In addition, Latent Dirichlet Allocation (LDA) is also widely used in news representation. Despite its low dimension, it still suffers from some drawbacks such as the need to set a predefined number of clusters and has difficulty recognizing new events. In this paper, a method based on multi-feature fusion is proposed, which combines the TF-IDF features, the named entity features, and the headline features to represent the news. Based on the representations, the incremental clustering method is used to cluster the news documents and to detect food safety events. Compared with the traditional methods, the proposed method achieved higher Precision, Recall, and F1 scores. The proposed method can help regulatory authorities to make decisions and improve the reputation of the government, whilst reducing social anxiety and economic losses.

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Section: Discussionmentioning

confidence: 99%

Food Safety Event Detection Based on Multi-Feature Fusion

et al. 2019

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“…All items are classified into keywords that mean the attributes of items. The term frequency inverse document frequency (TF-IDF) is a popular keyword extraction method that computes a weight based on the frequency of appearance of all item attributes [23]. The TF-IDF calculates the weight matrix for the attributes of items.…”

Section: B Recommendation Systemmentioning

confidence: 99%

Tree-Based Real-Time Advertisement Recommendation System in Online Broadcasting

2020

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The viewing time of media content per week through TV is still dominant. Users are exposed to numerous advertisements, such as commercials, electronic home shopping, product placement (PPL), and T-Commerce while watching TV programs. Most of the advertisement systems provide a good overview of products. However, traditional advertising services do not consider user preferences, meaning it is difficult to expect anything more than mere exposure to them. We can adopt a recommendation system to predict the preference. However, existing recommendation systems find it difficult to satisfy the realtime requirements of online broadcasting because of the large overhead incurred in preference prediction processes. In this paper, we propose a real-time recommendation system to provide personalized advertisements. The proposed system generates tree models based on user historical data. To reduce the overhead of preference prediction, we introduce a sorted HashMap that enables fast tree searches. For sophisticated preference prediction, the proposed system normalizes the users' preferences by considering the characteristics of their tree model. Finally, we conduct experiments to evaluate the performance of the proposed tree-based recommendation system.

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“…A method was proposed to produce brand nomenclatures and assorted nomenclatures, which were divided by the Chinese word segmentation algorithm. Based on the improved tf-idf algorithm, hot event problems could be found effectively [4]. The popular method of the characteristics of the processing is based on semantics.…”

Section: Feature Extractionmentioning

confidence: 99%

Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection

Wang

Liu

et al. 2019

Future Internet

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Social network services for self-media, such as Weibo, Blog, and WeChat Public, constitute a powerful medium that allows users to publish posts every day. Due to insufficient information transparency, malicious marketing of the Internet from self-media posts imposes potential harm on society. Therefore, it is necessary to identify news with marketing intentions for life. We follow the idea of text classification to identify marketing intentions. Although there are some current methods to address intention detection, the challenge is how the feature extraction of text reflects semantic information and how to improve the time complexity and space complexity of the recognition model. To this end, this paper proposes a machine learning method to identify marketing intentions from large-scale We-Media data. First, the proposed Latent Semantic Analysis (LSI)-Word2vec model can reflect the semantic features. Second, the decision tree model is simplified by decision tree pruning to save computing resources and reduce the time complexity. Finally, this paper examines the effects of classifier associations and uses the optimal configuration to help people efficiently identify marketing intention. Finally, the detailed experimental evaluation on several metrics shows that our approaches are effective and efficient. The F1 value can be increased by about 5%, and the running time is increased by 20%, which prove that the newly-proposed method can effectively improve the accuracy of marketing news recognition.

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Hot Topic Detection Based on a Refined TF-IDF Algorithm

Cited by 77 publications

References 16 publications

Food Safety Event Detection Based on Multi-Feature Fusion

Food Safety Event Detection Based on Multi-Feature Fusion

Tree-Based Real-Time Advertisement Recommendation System in Online Broadcasting

Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection

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