Short text similarity measurement using context‐aware weighted biterms

Yang, Shuiqiao; Huang, Guangyan; Ofoghi, Bahadorreza; Yearwood, John

doi:10.1002/cpe.5765

Cited by 24 publications

(8 citation statements)

References 25 publications

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“…Sentiment analysis is a research topic that analyzes people's sentiments, opinions, mental states, and emotions from data resources such as texts, images and videos that generated by human beings [11]. With the rapidly development of social media platforms such as Twitter and Facebook [17], it becomes possible to collect large-scale of text data from millions of users for the social medial sentiment analysis [1]. Most of the methods take the concept of supervised machine learning for social media sentimental analysis [6,7,10,12].…”

Section: Social Media Sentiment Analysismentioning

confidence: 99%

Examination of Community Sentiment Dynamics due to COVID-19 Pandemic: A Case Study from a State in Australia

et al. 2021

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The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has caused unprecedented impacts to people’s daily life around the world. Various measures and policies such as lockdown and social-distancing are implemented by governments to combat the disease during the pandemic period. These measures and policies as well as virus itself may cause different mental health issues to people such as depression, anxiety, sadness, etc. In this paper, we exploit the massive text data posted by Twitter users to analyse the sentiment dynamics of people living in the state of New South Wales (NSW) in Australia during the pandemic period. Different from the existing work that mostly focuses on the country-level and static sentiment analysis, we analyse the sentiment dynamics at the fine-grained local government areas (LGAs). Based on the analysis of around 94 million tweets that posted by around 183 thousand users located at different LGAs in NSW in 5 months, we found that people in NSW showed an overall positive sentimental polarity and the COVID-19 pandemic decreased the overall positive sentimental polarity during the pandemic period. The fine-grained analysis of sentiment in LGAs found that despite the dominant positive sentiment most of days during the study period, some LGAs experienced significant sentiment changes from positive to negative. This study also analysed the sentimental dynamics delivered by the hot topics in Twitter such as government policies (e.g. the Australia’s JobKeeper program, lockdown, social-distancing) as well as the focused social events (e.g. the Ruby Princess Cruise). The results showed that the policies and events did affect people’s overall sentiment, and they affected people’s overall sentiment differently at different stages.

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Section: Social Media Sentiment Analysismentioning

confidence: 99%

Examination of Community Sentiment Dynamics due to COVID-19 Pandemic: A Case Study from a State in Australia

et al. 2021

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“…Through analysis of the data over the past three years, they put forward some suggestions on attracting users in the COVID-19 crisis. At present, there are many methods that have been proposed for "network data mining", such as latent Dirichlet allocation (LDA) model [4,5], long short-term memory (LSTM) [6,7], Biterm method for short text distance measurement (BDM) [8,9], targeted aspects oriented topic modeling (TATM) [10], swarm intelligence (SI) algorithms [11], natural language processing (NLP) [12][13][14][15], etc., but they all have their advantages and disadvantages [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…Improving the research of specific text data mining based on Chinese to use the network text knowledge database effectively is also the focus of this field. The emotion analysis is another part of data mining [5,6,8,[16][17][18][19][20]. How to use scientific methods to mine and match the emotional tendency of short text data is the crucial point.…”

Section: Introductionmentioning

confidence: 99%

Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites

Xiong

Yan

Yao³

et al. 2022

Information

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In order to facilitate designers to explore the market demand trend of laptops and to establish a better “network users-market feedback mechanism”, we propose a design and research method of a short text mining tool based on the K-means clustering algorithm and Kano mode. An improved short text clustering algorithm is used to extract the design elements of laptops. Based on the traditional questionnaire, we extract the user’s attention factors, score the emotional tendency, and analyze the user’s needs based on the Kano model. Then, we select 10 laptops, process them by the improved algorithm, cluster the evaluation words and quantify the emotional orientation matching. Based on the obtained data, we design a visual interaction logic and usability test. These prove that the proposed method is feasible and effective.

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“…The form of texts such as micro-blogs, snippets, news titles, and question/answer pairs have become essential information carriers [3,15,4,9]. These texts are usually short, sparse, high-dimensional, and semantically diverse [30]. Short text analysis has wide applications, such as grouping similar documents (news, tweets, etc.…”

Section: Introductionmentioning

confidence: 99%

Representation Learning for Short Text Clustering

Yin¹,

Song²,

Yang³

et al. 2021

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Effective representation learning is critical for short text clustering due to the sparse, high-dimensional and noise attributes of short text corpus. Existing pre-trained models (e.g., Word2vec and BERT) have greatly improved the expressiveness for short text representations with more condensed, low-dimensional and continuous features compared to the traditional Bag-of-Words (BoW) model. However, these models are trained for general purposes and thus are suboptimal for the short text clustering task. In this paper, we propose two methods to exploit the unsupervised autoencoder (AE) framework to further tune the short text representations based on these pre-trained text models for optimal clustering performance. In our first method Structural Text Network Graph Autoencoder (STN-GAE), we exploit the structural text information among the corpus by constructing a text network, and then adopt graph convolutional network as encoder to fuse the structural features with the pre-trained text features for text representation learning. In our second method Soft Cluster Assignment Autoencoder (SCA-AE), we adopt an extra soft cluster assignment constraint on the latent space of autoencoder to encourage the learned text representations to be more clustering-friendly. We tested two methods on seven popular short text datasets, and the experimental results show that when only using the pre-trained model for short text clustering, BERT performs better than BoW and Word2vec. However, as long as we further tune the pre-trained representations, the proposed method like SCA-AE can greatly increase the clustering performance, and the accuracy improvement compared to use BERT alone could reach as much as 14%.

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Short text similarity measurement using context‐aware weighted biterms

Cited by 24 publications

References 25 publications

Examination of Community Sentiment Dynamics due to COVID-19 Pandemic: A Case Study from a State in Australia

Examination of Community Sentiment Dynamics due to COVID-19 Pandemic: A Case Study from a State in Australia

Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites

Representation Learning for Short Text Clustering

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