Fuzzy topic modeling approach for text mining over short text

Rashid, Junaid; Shah, Syed Muhammad Adnan; Irtaza, Aun

doi:10.1016/j.ipm.2019.102060

Cited by 81 publications

(32 citation statements)

References 23 publications

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“…We believe that improving coverage and accuracy of query classification might be beneficial for the STD cache hit ratio. As future work, we plan to employ other topic-modeling techniques which are tailored for short text [38]. We would also like to use this cache in synergy with other caches, e.g., those storing posting lists of frequently requested terms.…”

Section: Discussionmentioning

confidence: 99%

Topical result caching in web search engines

Mele

Tonellotto

Perego

2020

Information Processing & Management

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Caching search results is employed in information retrieval systems to expedite query processing and reduce back-end server workload. Motivated by the observation that queries belonging to different topics have different temporal-locality patterns, we investigate a novel caching model called STD (Static-Topic-Dynamic cache). It improves traditional SDC (Static-Dynamic Cache) that stores in a static cache the results of popular queries and manages the dynamic cache with a replacement policy for intercepting the temporal variations in the query stream.Our proposed caching scheme includes another layer for topic-based caching, where the entries are allocated to different topics (e.g., weather, education). The results of queries characterized by a topic are kept in the fraction of the cache dedicated to it. This permits to adapt the cache-space utilization to the temporal locality of the various topics and reduces cache misses due to those queries that are neither sufficiently popular to be in the static portion nor requested within short-time intervals to be in the dynamic portion.We simulate different configurations for STD using two real-world query streams. Experiments demonstrate that our approach outperforms SDC with an increase up to 3% in terms of hit rates, and up to 36% of gap reduction w.r.t. SDC from the theoretical optimal caching algorithm.

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Section: Discussionmentioning

confidence: 99%

Topical result caching in web search engines

Mele

Tonellotto

Perego

2020

Information Processing & Management

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“…The text was cleaned by removing numbers and punctuation as well as stop words; it was also transformed into lower case letters to decrease distinctions in the same words. Second, tokenization, which helps to chop up a sequence of characters into meaningful pieces (i.e., tokens) [26], was used to filter out certain characters, punctuation, etc. Tokenization transformed the text into attributes.…”

Section: Methodsmentioning

confidence: 99%

Sentiment Analysis towards Actionable Intelligence via Deep Learning

Eletter¹

2020

TEM Journal

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The exponential growth of unstructured data and the ability of businesses to utilize such data in decision-making have led to competitive advantages. The knowledge provided by analyzing unstructured data is crucial for product developers or service providers because it might affect the sustainability of the business. Sentiment analysis is used to gain an understanding of the attitudes, opinions, and emotions expressed within an online review. Naïve Bayes (NB), logistic regression (LR), decision trees (DT), deep learning (DL), and support vector machines (SVM) were used to build a classification model. In the data mining settings, the classification accuracy is the best metric to highlight the best classifier. The DL classifier outperformed other models in terms of accuracy rate. Classifying customers' feelings toward a product or service is critical for providing actionable insights. Utilizing such models will help to analyze huge volumes of reviews, saving both time and costs.

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“…In the past decades, researchers have developed many extensions built on the above formulation, and they have been successfully applied to deal with various problems as well as various kinds of text data, e.g., topic correlations [5,23,27,30], dynamic topics varying over time [4,51], and sparse topics within short texts [9,16,31,44], etc. Commonly, the popular model inference methods include variational inference often with mean-field approximations [6,24], Gibbs sampling [18], and hybrid methods [29,38].…”

Section: Traditional Topic Modelmentioning

confidence: 99%

Extracting nonlinear neural topics with neural variational bayes

Wang

Ouyang

et al. 2021

World Wide Web

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Recently, topic modeling has been upgraded by neural variational inference, which simultaneously allows the model structures deeper and proposes efficient update rules with the reparameterization trick. We formally call this recent new art as neural topic model. In this paper, we investigate a problem of neural topic models, where they formulate topic embeddings and measure the word weights within topics by linear transformation between topic and word embeddings, resulting in redundant and inaccurate topic representations.To solve this problem, we propose a novel neural topic model, namely Generative Model with Nonlinear Neural Topics (GMNNT). The insight of GMNNT is to replace the topic embeddings with neural networks of topics, named neural topic, so as to capture nonlinear relationships between words in the embedding space, enabling to induce more accurate topic representations. We derive the inference process of GMNNT under the framework of neural variational inference. Extensive empirical studies have been conducted on several widely used collections of documents, including datasets of both short texts and normal long texts. The experimental results validate that GMNNT can output more semantically coherent topics compared with traditional topic models and neural topic models.

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Fuzzy topic modeling approach for text mining over short text

Cited by 81 publications

References 23 publications

Topical result caching in web search engines

Topical result caching in web search engines

Sentiment Analysis towards Actionable Intelligence via Deep Learning

Extracting nonlinear neural topics with neural variational bayes

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