Distributed Latent Dirichlet Allocation on Streams

2021 ◽  
Vol 16 (1) ◽  
pp. 1-20
Author(s):  
Yunyan Guo ◽  
Jianzhong Li
Keyword(s):  
Real Time ◽  
Language Processing ◽  
Real World ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Real Life ◽  
Streaming Data ◽  
Real World Datasets ◽  
Dirichlet Allocation ◽  
Online Inference

Latent Dirichlet Allocation (LDA) has been widely used for topic modeling, with applications spanning various areas such as natural language processing and information retrieval. While LDA on small and static datasets has been extensively studied, several real-world challenges are posed in practical scenarios where datasets are often huge and are gathered in a streaming fashion. As the state-of-the-art LDA algorithm on streams, Streaming Variational Bayes (SVB) introduced Bayesian updating to provide a streaming procedure. However, the utility of SVB is limited in applications since it ignored three challenges of processing real-world streams: topic evolution , data turbulence , and real-time inference . In this article, we propose a novel distributed LDA algorithm—referred to as StreamFed-LDA— to deal with challenges on streams. For topic modeling of streaming data, the ability to capture evolving topics is essential for practical online inference. To achieve this goal, StreamFed-LDA is based on a specialized framework that supports lifelong (continual) learning of evolving topics. On the other hand, data turbulence is commonly present in streams due to real-life events. In that case, the design of StreamFed-LDA allows the model to learn new characteristics from the most recent data while maintaining the historical information. On massive streaming data, it is difficult and crucial to provide real-time inference results. To increase the throughput and reduce the latency, StreamFed-LDA introduces additional techniques that substantially reduce both computation and communication costs in distributed systems. Experiments on four real-world datasets show that the proposed framework achieves significantly better performance of online inference compared with the baselines. At the same time, StreamFed-LDA also reduces the latency by orders of magnitudes in real-world datasets.

A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura

2021 ◽  
pp. 016555152110077
Author(s):  
Sulong Zhou ◽  
Pengyu Kan ◽  
Qunying Huang ◽  
Janet Silbernagel
Keyword(s):  
Social Media ◽  
Real Time ◽  
Language Processing ◽  
Disaster Response ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Situational Awareness ◽  
High Performing ◽  
Latent Topics ◽  
Dirichlet Allocation

Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.

scholarly journals Topic Modeling for Keyword Extraction: using Natural Language Processing methods for keyword extraction in Portal Min@s

2015 ◽  
Vol 23 (3) ◽  
pp. 695 ◽  
Author(s):  
Arnaldo Candido Junior ◽  
Célia Magalhães ◽  
Helena Caseli ◽  
Régis Zangirolami
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Keyword Extraction ◽  
Processing Methods ◽  
Dirichlet Allocation

<p style="margin-bottom: 0cm; line-height: 100%;" align="justify"> </p><p>Este artigo tem o objetivo da avaliar a aplicação de dois métodos automáticos eficientes na extração de palavras-chave, usados pelas comunidades da Linguística de <em>Corpus </em>e do Processamento da Língua Natural para gerar palavras-chave de textos literários: o <em>WordSmith Tools </em>e o <em>Latent Dirichlet Allocation </em>(LDA). As duas ferramentas escolhidas para este trabalho têm suas especificidades e técnicas diferentes de extração, o que nos levou a uma análise orientada para a sua performance. Objetivamos entender, então, como cada método funciona e avaliar sua aplicação em textos literários. Para esse fim, usamos análise humana, com conhecimento do campo dos textos usados. O método LDA foi usado para extrair palavras-chave por meio de sua integração com o <em>Portal Min@s: Corpora de Fala e Escrita</em>, um sistema geral de processamento de <em>corpora</em>, concebido para diferentes pesquisas de Linguística de <em>Corpus</em>. Os resultados do experimento confirmam a eficácia do WordSmith Tools e do LDA na extração de palavras-chave de um <em>corpus </em>literário, além de apontar que é necessária a análise humana das listas em um estágio anterior aos experimentos para complementar a lista gerada automaticamente, cruzando os resultados do WordSmith Tools e do LDA. Também indicam que a intuição linguística do analista humano sobre as listas geradas separadamente pelos dois métodos usados neste estudo foi mais favorável ao uso da lista de palavras-chave do WordSmith Tools.</p>

scholarly journals Analyzing U.S. Army Officer Evaluation Reports with Natural Language Processing: A Log-Odds and Latent Dirichlet Allocation Exploration

2019 ◽  
Vol 7 (1) ◽  
pp. 44-55
Author(s):  
Heidy Shi ◽  
John Caddell ◽  
Julia Lensing
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Language Processing ◽  
Text Analysis ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Data Set ◽  
Army Officer ◽  
Log Odds ◽  
Dirichlet Allocation

Each job field (branch) in the Army requires a unique set of skills and talents of the officers assigned. Officers who demonstrate the required skills are often more successful in their assigned branch. To better understand how success is described across branches, research was conducted using text mining and text analysis of a data set of Officer Evaluation Reports (OERs). This research looked for common trends and discrepancies across varying branches and like groups of branches by analyzing the narrative portion of OERs. Text analysis methods examined words and bigrams commonly used to describe varying degrees of performance by officers. Topic modeling using Latent Dirichlet Allocation (LDA) was also conducted on top rated narratives to investigate trends and discrepancies in clustering narratives. Findings show that qualitative narratives for the top two performance designations fail to differentiate between officers’ varying levels of performance regardless of branch.

Topic prediction and knowledge discovery based on integrated topic modeling and deep neural networks approaches

2021 ◽  
pp. 1-17
Author(s):  
Zeinab Shahbazi ◽  
Yung-Cheol Byun
Keyword(s):  
Machine Learning ◽  
Knowledge Discovery ◽  
Real World ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Machine Learning Techniques ◽  
Probabilistic Latent Semantic Analysis ◽  
Learning Techniques ◽  
Dirichlet Allocation

Understanding the real-world short texts become an essential task in the recent research area. The document deduction analysis and latent coherent topic named as the important aspect of this process. Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) are suggested to model huge information and documents. This type of contexts’ main problem is the information limitation, words relationship, sparsity, and knowledge extraction. The knowledge discovery and machine learning techniques integrated with topic modeling were proposed to overcome this issue. The knowledge discovery was applied based on the hidden information extraction to increase the suitable dataset for further analysis. The integration of machine learning techniques, Artificial Neural Network (ANN) and Long Short-Term (LSTM) are applied to anticipate topic movements. LSTM layers are fed with latent topic distribution learned from the pre-trained Latent Dirichlet Allocation (LDA) model. We demonstrate general information from different techniques applied in short text topic modeling. We proposed three categories based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation using representative design and analysis of all categories’ performance in different tasks. Finally, the proposed system evaluates with state-of-art methods on real-world datasets, comprises them with long document topic modeling algorithms, and creates a classification framework that considers further knowledge and represents it in the machine learning pipeline.

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

scholarly journals Application of Latent Dirichlet Allocation (LDA) for clustering financial tweets

2021 ◽  
Vol 297 ◽  
pp. 01071
Author(s):  
Sifi Fatima-Zahrae ◽  
Sabbar Wafae ◽  
El Mzabi Amal
Keyword(s):  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Sentiment Classification ◽  
Research Areas ◽  
Preprocessing Method ◽  
Long Time ◽  
Standard Text ◽  
The Given ◽  
Dirichlet Allocation

Sentiment classification is one of the hottest research areas among the Natural Language Processing (NLP) topics. While it aims to detect sentiment polarity and classification of the given opinion, requires a large number of aspect extractions. However, extracting aspect takes human effort and long time. To reduce this, Latent Dirichlet Allocation (LDA) method have come out recently to deal with this issue.In this paper, an efficient preprocessing method for sentiment classification is presented and will be used for analyzing user’s comments on Twitter social network. For this purpose, different text preprocessing techniques have been used on the dataset to achieve an acceptable standard text. Latent Dirichlet Allocation has been applied on the obtained data after this fast and accurate preprocessing phase. The implementation of different sentiment analysis methods and the results of these implementations have been compared and evaluated. The experimental results show that the combined uses of the preprocessing method of this paper and Latent Dirichlet Allocation have an acceptable results compared to other basic methods.

scholarly journals Spam Diffusion in Social Networking Media using Latent Dirichlet Allocation

2019 ◽  
Vol 8 (12) ◽  
pp. 881-885
Keyword(s):  
Online Social Networks ◽  
Topic Modeling ◽  
Information Diffusion ◽  
Latent Dirichlet Allocation ◽  
Good Accuracy ◽  
Ground Truth ◽  
Online Social Media ◽  
Diffusion Dynamics ◽  
Dirichlet Allocation

Like web spam has been a major threat to almost every aspect of the current World Wide Web, similarly social spam especially in information diffusion has led a serious threat to the utilities of online social media. To combat this challenge the significance and impact of such entities and content should be analyzed critically. In order to address this issue, this work usedTwitter as a case study and modeled the contents of information through topic modeling and coupled it with the user oriented feature to deal it with a good accuracy. Latent Dirichlet Allocation (LDA) a widely used topic modeling technique is applied to capture the latent topics from the tweets’ documents. The major contribution of this work is twofold: constructing the dataset which serves as the ground-truth for analyzing the diffusion dynamics of spam/non-spam information and analyzing the effects of topics over the diffusibility. Exhaustive experiments clearly reveal the variation in topics shared by the spam and nonspam tweets. The rise in popularity of online social networks, not only attracts legitimate users but also the spammers. Legitimate users use the services of OSNs for a good purpose i.e., maintaining the relations with friends/colleagues, sharing the information of interest, increasing the reach of their business through advertisings

scholarly journals Be Relevant, Non-Redundant, and Timely: Deep Reinforcement Learning for Real-Time Event Summarization

2020 ◽  
Vol 34 (05) ◽  
pp. 9410-9417
Author(s):  
Min Yang ◽  
Chengming Li ◽  
Fei Sun ◽  
Zhou Zhao ◽  
Ying Shen ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Language Processing ◽  
Semantic Features ◽  
Unified Framework ◽  
Time Step ◽  
Dynamic Memory ◽  
The Arts ◽  
Real World Datasets ◽  
Event Summarization

Real-time event summarization is an essential task in natural language processing and information retrieval areas. Despite the progress of previous work, generating relevant, non-redundant, and timely event summaries remains challenging in practice. In this paper, we propose a Deep Reinforcement learning framework for real-time Event Summarization (DRES), which shows promising performance for resolving all three challenges (i.e., relevance, non-redundancy, timeliness) in a unified framework. Specifically, we (i) devise a hierarchical cross-attention network with intra- and inter-document attentions to integrate important semantic features within and between the query and input document for better text matching. In addition, relevance prediction is leveraged as an auxiliary task to strengthen the document modeling and help to extract relevant documents; (ii) propose a multi-topic dynamic memory network to capture the sequential patterns of different topics belonging to the event of interest and temporally memorize the input facts from the evolving document stream, avoiding extracting redundant information at each time step; (iii) consider both historical dependencies and future uncertainty of the document stream for generating relevant and timely summaries by exploiting the reinforcement learning technique. Experimental results on two real-world datasets have demonstrated the advantages of DRES model with significant improvement in generating relevant, non-redundant, and timely event summaries against the state-of-the-arts.

Sign in / Sign up

Export Citation Format

Share Document