Vector Representation of Words for Detecting Topic Trends over Short Texts

He, Liyan; Du, Yajun; Zhang, Lei

doi:10.2991/mmsa-18.2018.97

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Once the number of topics has been determined, we proceed to find vectorembedding representations of tweets, as they have been previously shown to yield superior results in topic modelling with respect to LDA [9]. Here, we use the word2vec-based [17,7,22] FastText model [4], which essentially uses sub-word information to enrich embeddings generated by a neural network that predicts neighbouring words.…”

Section: Methodsmentioning

confidence: 99%

Event detection in Colombian security Twitter news using fine-grained latent topic analysis

Vargas-Calderón,

Parra-A.,

Camargo

et al. 2019

Preprint

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Cultural and social dynamics are important concepts that must be understood in order to grasp what a community cares about. To that end, an excellent source of information on what occurs in a community is the news, especially in recent years, when mass media giants use social networks to communicate and interact with their audience. In this work, we use a method to discover latent topics in tweets from Colombian Twitter news accounts in order to identify the most prominent events in the country. We pay particular attention to security, violence and crime-related tweets because of the violent environment that surrounds Colombian society. The latent topic discovery method that we use builds vector representations of the tweets by using FastText and finds clusters of tweets through the K-means clustering algorithm. The number of clusters is found by measuring the CV coherence for a range of number of topics of the Latent Dirichlet Allocation (LDA) model. We finally use Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction to visualise the tweets vectors. Once the clusters related to security, violence and crime are identified, we proceed to apply the same method within each cluster to perform a fine-grained analysis in which specific events mentioned in the news are grouped together. Our method is able to discover event-specific sets of news, which is the baseline to perform an extensive analysis of how people engage in Twitter threads on the different types of news, with an emphasis on security, violence and crime-related tweets.

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Section: Methodsmentioning

confidence: 99%

Event detection in Colombian security Twitter news using fine-grained latent topic analysis

Vargas-Calderón,

Parra-A.,

Camargo

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Bengio et al (2003) proposed a neural network-based language model (NNLM) and introduced the concept of "word embedding." Afterward, word vectors were proposed by He et al (2018) to train the surrounding context of a target word and carry contextual information about the word (Jiang & He, 2020;Lan et al, 2021). Barnickel et al (2009) proposed the SENNA model for the purpose of generating word vector representations.…”

Section: Figure 2 Relationship Between MI and Information Entropy Dis...mentioning

confidence: 99%

Contextual Word2Vec Model for Understanding Chinese Out of Vocabularies on Online Social Media

et al. 2022

International Journal on Semantic Web and Information Systems

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In this chapter, the authors propose to use contextual Word2Vec model for understanding OOV (out of vocabulary). The OOV is extracted by using left-right entropy and point information entropy. They choose to use Word2Vec to construct the word vector space and CBOW (continuous bag of words) to obtain the contextual information of the words. If there is a word that has similar contextual information to the OOV, the word can be used to understand the OOV. They chose the Weibo corpus as the dataset for the experiments. The results show that the proposed model achieves 97.10% accuracy, which is better than Skip-Gram by 8.53%.

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Vector Representation of Words for Detecting Topic Trends over Short Texts

Cited by 2 publications

References 21 publications

Event detection in Colombian security Twitter news using fine-grained latent topic analysis

Event detection in Colombian security Twitter news using fine-grained latent topic analysis

Contextual Word2Vec Model for Understanding Chinese Out of Vocabularies on Online Social Media

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