Domain identification for intention posts on online social media

Luong, Thai-Le; Truong, Quoc; Dang, Hai-Trieu; Phan, Xuan-Hieu

doi:10.1145/3011077.3011134

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…For this reason, many scholars have proposed to train topic models on a syndicated long text in the same field and then infer the essay to help short text learning tasks [25,26]. However, on the highly dynamic social platforms such as Weibo, new topics are constantly appearing and user preferences change constantly.…”

Section: The Reason For the Proposed Methodmentioning

confidence: 99%

“…The goal of this step is to get the relevant documents and a high recall rate. Existing technologies, such as reverse indexes used in information retrieval, sensitive areas of high-dimensional data points, and APIs directly from existing search engines can be utilized to implement the process [26]. If you want to make sure the recall rate is high, you have to set the number of returned documents to be quite large, for example, tens or hundreds of documents, but the resulting long text has a significant noise disturbance.…”

Section: Long Text Processing Methodmentioning

confidence: 99%

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CLDA: An Effective Topic Model for Mining User Interest Preference under Big Data Background

Qiu

2018

Complexity

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In the present big data background, how to effectively excavate useful information is the problem that big data is facing now. The purpose of this study is to construct a more effective method of mining interest preferences of users in a particular field in the context of today's big data. We mainly use a large number of user text data from microblog to study. LDA is an effective method of text mining, but it will not play a very good role in applying LDA directly to a large number of short texts in microblog. In today's more effective topic modeling project, short texts need to be aggregated into long texts to avoid data sparsity. However, aggregated short texts are mixed with a lot of noise, reducing the accuracy of mining the user's interest preferences. In this paper, we propose Combining Latent Dirichlet Allocation (CLDA), a new topic model that can learn the potential topics of microblog short texts and long texts simultaneously. The data sparsity of short texts is avoided by aggregating long texts to assist in learning short texts. Short text filtering long text is reused to improve mining accuracy, making long texts and short texts effectively combined. Experimental results in a real microblog data set show that CLDA outperforms many advanced models in mining user interest, and we also confirm that CLDA also has good performance in recommending systems.

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Section: The Reason For the Proposed Methodmentioning

confidence: 99%

Section: Long Text Processing Methodmentioning

confidence: 99%

CLDA: An Effective Topic Model for Mining User Interest Preference under Big Data Background

Qiu

2018

Complexity

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“…Intention classification is helpful for commercial applications such as target advertising in social media (Luong et al, 2016). One of the early studies by Chen et al (2013) formulates the intention identification from posts in multi-domain discussion forums as a binary classification task (i.e., explicit intent and non-intent posts with a specific focus on buying intention).…”

Section: Additional Related Workmentioning

confidence: 99%

Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing

Ji¹

2022

Findings of the Association for Computational Linguistics: EMNLP 2022

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Recent applications of natural language processing techniques to suicidal ideation detection and risk assessment frame the detection or assessment task as a text classification problem. Recent advances have developed many models, especially deep learning models, to boost predictive performance. Though the performance (in terms of aggregated evaluation scores) is improving, this position paper urges that better intention understanding is required for reliable suicidal risk assessment with computational methods. This paper reflects the state of natural language processing applied to suicide-associated text classification tasks, differentiates suicidal risk assessment and intention understanding, and points out potential limitations of sentiment features and pretrained language models in suicidal intention understanding. Besides, it urges the necessity for sequential intention understanding and risk assessment, discusses some critical issues in evaluation such as uncertainty, and studies the lack of benchmarks.

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“…The fuzzy comprehensive evaluation method has been used to combine with the analytical hierarchy process (AHP) to assess the insured wishes index system [11,12]. Luong et al [47] designed a Bayesian networkbased context-specific implicit intention recognition model to mine the user's implicit intention. Chen et al [48] used a semi-supervised user's question asking framework to detect the user's implicit intention from the community question answering (CQA) Yahoo!…”

Section: Implicit Intentionsmentioning

confidence: 99%

“…In [44] the authors depicted the intent recommendations using two reallife datasets Movie lens and Tmall. The articles [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61] used commercial, personal assistant clicks, and views log as a dataset to predict intention with its real-time context.…”

Section: Search Engine Log Datamentioning

confidence: 99%

Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challenges

Rashid

Farooq

Abid

et al. 2021

Complex Intell. Syst.

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Intention mining is a promising research area of data mining that aims to determine end-users’ intentions from their past activities stored in the logs, which note users’ interaction with the system. Search engines are a major source to infer users’ past searching activities to predict their intention, facilitating the vendors and manufacturers to present their products to the user in a promising manner. This area has been consistently getting pertinence with an increasing trend for online purchasing. Noticeable research work has been accomplished in this area for the last two decades. There is no such systematic literature review available that provides a comprehensive review in intension mining domain to the best of our knowledge. This article presents a systematic literature review based on 109 high-quality research papers selected after rigorous screening. The analysis reveals that there exist eight prominent categories of intention. Furthermore, a taxonomy of the approaches and techniques used for intention mining have been discussed in this article. Similarly, six important types of data sets used for this purpose have also been discussed in this work. Lastly, future challenges and research gaps have also been presented for the researchers working in this domain.

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Domain identification for intention posts on online social media

Cited by 8 publications

References 8 publications

CLDA: An Effective Topic Model for Mining User Interest Preference under Big Data Background

CLDA: An Effective Topic Model for Mining User Interest Preference under Big Data Background

Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing

Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challenges

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