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Hierarchical Latent Word Clustering [article]

Halid Ziya Yerebakan, Fitsum Reda, Yiqiang Zhan, Yoshihisa Shinagawa
<span title="2016-01-20">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper presents a new Bayesian non-parametric model by extending the usage of Hierarchical Dirichlet Allocation to extract tree structured word clusters from text data.  ...  The inference algorithm of the model collects words in a cluster if they share similar distribution over documents.  ...  Conclusion We presented Hierarchical Latent Word Clustering as a non-parametric hierarchical clustering structure on words.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1601.05472v1">arXiv:1601.05472v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4lzd3g6as5hkfaq6ydxssc2jye">fatcat:4lzd3g6as5hkfaq6ydxssc2jye</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200823012403/https://arxiv.org/pdf/1601.05472v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/23/46/23462737d24edfb10250e736e751f0ee77a50734.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1601.05472v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Analysis of Valuable Clustering Techniques for Deep Web Access and Navigation

Qurat ul, Asma Sajid, Uzma Jamil
<span title="">2018</span> <i title="The Science and Information Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2yzw5hsmlfa6bkafwsibbudu64" style="color: black;">International Journal of Advanced Computer Science and Applications</a> </i> &nbsp;
Analysis and comparison of Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), and Hierarchical and K-means method have been carried out and valuable factors for clustering in deep web have  ...  Different clustering techniques offer a simple way to analyze large volume of non-indexed content.  ...  Dendrograms are used to represent hierarchical clustering. 2) Types of clustering a) Hierarchical Clustering: Sr# Latent Semantic Analysis Probabilistic Latent Semantic Analysis 1 Highest Gaussian Error  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2018.090128">doi:10.14569/ijacsa.2018.090128</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ojmqqbrxbfdx5dfr7sucjtz5p4">fatcat:ojmqqbrxbfdx5dfr7sucjtz5p4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180719180309/http://thesai.org/Downloads/Volume9No1/Paper_28-Analysis_of_Valuable_Clustering_Techniques.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/03/f1/03f12e5d9532214d731e04db568431a53d1843db.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2018.090128"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya

Leacky Muchene, Wende Safari, Diego Raphael Amancio
<span title="2021-01-07">2021</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
Further, the use of hierarchical clustering in the second stage reduces the discovered latent topics to clusters of homogeneous topics.  ...  To more succinctly present the topics, in the second stage, hierarchical clustering with Hellinger distance was applied to derive the final clusters of topics.  ...  Hierarchical clustering of LDA-derived topics As highlighted in the methodology section, we performed hierarchical clustering in order to reduce the number of topics into a smaller set using hierarchical  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0243208">doi:10.1371/journal.pone.0243208</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33411774">pmid:33411774</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aqn2jyoacffdvdl5wym4kuue2y">fatcat:aqn2jyoacffdvdl5wym4kuue2y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210114050046/https://storage.googleapis.com/plos-corpus-prod/10.1371/journal.pone.0243208/1/pone.0243208.pdf?X-Goog-Algorithm=GOOG4-RSA-SHA256&amp;X-Goog-Credential=wombat-sa%40plos-prod.iam.gserviceaccount.com%2F20210114%2Fauto%2Fstorage%2Fgoog4_request&amp;X-Goog-Date=20210114T050042Z&amp;X-Goog-Expires=3600&amp;X-Goog-SignedHeaders=host&amp;X-Goog-Signature=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" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/99/99/9999c724be2a087e90221dd5d8c7178030f231f6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0243208"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a>

Hierarchical Latent Tree Analysis for Topic Detection [chapter]

Tengfei Liu, Nevin L. Zhang, Peixian Chen
<span title="">2014</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
The states of the latent variables represent clusters of documents and they are interpreted as topics.  ...  The words that best distinguish a cluster from other clusters are selected to characterize the topic.  ...  The bottom level consists of the word variables. We call the model a hierarchical latent tree model (HLTM).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-662-44851-9_17">doi:10.1007/978-3-662-44851-9_17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/njvh6ladxvczpdhspp6njifwau">fatcat:njvh6ladxvczpdhspp6njifwau</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200326212604/https://link.springer.com/content/pdf/10.1007%2F978-3-662-44851-9_17.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/99/6a/996afee2087a515de92842b2d0c9f406ab3e5e56.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-662-44851-9_17"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

HIERARCHICAL LATENT SEMANTIC CLASS EXTRACTION USING ASYMMETRIC TERM SIMILARITIES

Alberto Muñoz, Javier González
<span title="">2012</span> <i title="Behaviormetric Society of Japan"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dem5zlrvj5fozg4qkmp46jeb4a" style="color: black;">Behaviormetrika</a> </i> &nbsp;
In this work we propose a hierarchical latent topic extraction method that exploits the information contained in asymmetric term similarity matrices.  ...  However such models do not provide neither Euclidean coordinates for terms nor a hierarchical structure to organize the latent semantic classes, which makes difficult to visualize the information under  ...  First of all, the word maps shown in Figures 5, 7 and 9 do not show any cluster structure that could help to reveal the latent classes of the data set.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2333/bhmk.39.91">doi:10.2333/bhmk.39.91</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gwib72dymbazvek3wqadldtrne">fatcat:gwib72dymbazvek3wqadldtrne</a> </span>
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Latent Semantics of Action Verbs Reflect Phonetic Parameters of Intensity and Emotional Content

Michael Kai Petersen, Johan J Bolhuis
<span title="2015-04-07">2015</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
Applying hierarchical clustering to identify common structures across the two text corpora, the verbs largely divide into combined mouth and hand movements versus emotional expressions.  ...  Conjuring up our thoughts, language reflects statistical patterns of word co-occurrences which in turn come to describe how we perceive the world.  ...  Selecting the action verbs which were hierarchically clustered similarly based on both the HAWIK and TASA adjacency matrices, the words were annotated with their corresponding user rated word norms available  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0121575">doi:10.1371/journal.pone.0121575</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25849977">pmid:25849977</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4388570/">pmcid:PMC4388570</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mxmexuqfw5asrcenseew6egb5u">fatcat:mxmexuqfw5asrcenseew6egb5u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171010173547/http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0121575&amp;type=printable" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ee/e3/eee3f428ddc644fadbb423e705fc23c55bd617aa.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0121575"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4388570" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Latent Tree Analysis [article]

Nevin L. Zhang, Leonard K. M. Poon
<span title="2016-10-01">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
It provides new and fruitful perspectives on a number of machine learning areas, including cluster analysis, topic detection, and deep probabilistic modeling.  ...  Latent tree analysis seeks to model the correlations among a set of random variables using a tree of latent variables.  ...  Such models are called hierarchical latent tree models (HLTMs) and the process of learning HLTMs is called hierarchical latent tree analysis (HLTA).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1610.00085v1">arXiv:1610.00085v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7kwneqng7jdfva3ny4bi2ap2la">fatcat:7kwneqng7jdfva3ny4bi2ap2la</a> </span>
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Hierarchical Latent Semantic Mapping for Automated Topic Generation [article]

Guorui Zhou, Guang Chen
<span title="2015-11-26">2015</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
HLSM calculates the association between each pair of words in the latent topic space, then constructs a unipartite network of words with this association and hierarchically generates topics from this network  ...  Inspired by these algorithms, in this paper, we propose a novel method named Hierarchical Latent Semantic Mapping (HLSM), which automatically generates topics from corpus.  ...  HIERARCHICAL LATENT SEMANTIC MAPPING Hierarchical Latent Semantic Mapping (HLSM) is a network approach to topic modeling.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1511.03546v4">arXiv:1511.03546v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ii6jcnzpvfbn7phdsomhyxomzm">fatcat:ii6jcnzpvfbn7phdsomhyxomzm</a> </span>
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Latent Tree Models for Hierarchical Topic Detection [article]

Peixian Chen, Nevin L. Zhang, Tengfei Liu, Leonard K.M. Poon, Zhourong Chen, Farhan Khawar
<span title="2016-12-21">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways.  ...  Specifically, we model document collections using a class of graphical models called hierarchical latent tree models (HLTMs).  ...  HLTA produces a hierarchy with word variables at the bottom and multiple levels of latent variables at top. It is hence related to hierarchical variable clustering.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1605.06650v2">arXiv:1605.06650v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mv5tgr4bhreurcoysos7kcinta">fatcat:mv5tgr4bhreurcoysos7kcinta</a> </span>
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A Novel Document Generation Process for Topic Detection based on Hierarchical Latent Tree Models [article]

Peixian Chen, Zhourong Chen, Nevin L. Zhang
<span title="2019-06-28">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a novel document generation process based on hierarchical latent tree models (HLTMs) learned from data.  ...  An HLTM has a layer of observed word variables at the bottom and multiple layers of latent variables on top.  ...  Hence the model is called a hierarchical latent tree model (HLTM) .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1712.04116v3">arXiv:1712.04116v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vy2xnblpyjcwfeo2jh2uvzqmay">fatcat:vy2xnblpyjcwfeo2jh2uvzqmay</a> </span>
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Legal Documents Clustering and Summarization using Hierarchical Latent Dirichlet Allocation

Ravi kumar Venkatesh
<span title="2013-03-01">2013</span> <i title="Institute of Advanced Engineering and Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3egptraynbg7xelu7qh6lwtd6i" style="color: black;">IAES International Journal of Artificial Intelligence (IJ-AI)</a> </i> &nbsp;
[12] introduced hierarchically supervised latent Dirichlet allocation (HSLDA), a model for hierarchically and multiply labeled bag-of-word data.  ...  [13] proposed a hierarchical generative model for textual data, where words are grouped to form a topic using co occurrence basis and are arranged in a hierarchically to cluster and categorize the documents  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/ij-ai.v2i1.1186">doi:10.11591/ij-ai.v2i1.1186</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uvsddohwsjfbvl4uic6vwbqzzm">fatcat:uvsddohwsjfbvl4uic6vwbqzzm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190427191433/http://www.iaescore.com/journals/index.php/IJAI/article/download/1361/pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2c/3a/2c3a14dab802d5550f37c26c39b6854c54d28f56.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/ij-ai.v2i1.1186"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Hierarchial chunking in sentence processing

W. J. M. Levelti
<span title="">1970</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/icivrhnmgzcdjerqdqvofcj4xa" style="color: black;">Perception &amp; Psychophysics</a> </i> &nbsp;
Hierarchical Clustering Scheme Analysis In order to investigate whether or not there is a latent hierarchical structure underlying these data, we applied S.  ...  This is another indication for the latent hierarchical structure in the corresponding data matrices.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3758/bf03210182">doi:10.3758/bf03210182</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ja35jaejc5a7rn3cwss2rizaua">fatcat:ja35jaejc5a7rn3cwss2rizaua</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809095337/http://www.mpi.nl/world/materials/publications/levelt/Levelt_Hierarchial_Chunking_1970.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a3/2e/a32ef28ec627ecd823b25ab923cb7026e6a1ee46.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3758/bf03210182"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Hierarchical Latent Semantic Mapping for automated topic generation

Guorui Zhou, Guang Chen
<span title="">2016</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/z5doux3go5eytny32r6tuwvnrm" style="color: black;">2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)</a> </i> &nbsp;
HLSM calculates the association between each pair of words in the latent topic space, then constructs a unipartite network of words with this association and hierarchically generates topics from this network  ...  Inspired by these algorithms, in this paper, we propose a novel method named Hierarchical Latent Semantic Mapping (HLSM), which automatically generates topics from corpus.  ...  Hierarchical Latent Semantic Mapping Hierarchical Latent Semantic Mapping (HLSM) is a network approach to topic modeling.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/snpd.2016.7515878">doi:10.1109/snpd.2016.7515878</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/snpd/ZhouC16.html">dblp:conf/snpd/ZhouC16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/k6642rhwfncgjfu3suf7sxz27e">fatcat:k6642rhwfncgjfu3suf7sxz27e</a> </span>
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Hierarchical Latent Semantic Mapping for Automated Topic Generation

Guorui Zhou, Guang Chen
<span title="">2016</span> <i title="Atlantis Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mitejrukingkdiyzijpbo7msre" style="color: black;">International Journal of Networked and Distributed Computing (IJNDC)</a> </i> &nbsp;
HLSM calculates the association between each pair of words in the latent topic space, then constructs a unipartite network of words with this association and hierarchically generates topics from this network  ...  Inspired by these algorithms, in this paper, we propose a novel method named Hierarchical Latent Semantic Mapping (HLSM), which automatically generates topics from corpus.  ...  Hierarchical Latent Semantic Mapping Hierarchical Latent Semantic Mapping (HLSM) is a network approach to topic modeling.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2991/ijndc.2016.4.2.6">doi:10.2991/ijndc.2016.4.2.6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/j3433jz22rcwvixstz5czttasq">fatcat:j3433jz22rcwvixstz5czttasq</a> </span>
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Improving the Accuracy in Sentiment Classification in the Light of Modelling the Latent Semantic Relations

Nina Rizun, Yurii Taranenko, Wojciech Waloszek
<span title="2018-12-04">2018</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dmr4kpn2yreovpdxpdiqtjcrnu" style="color: black;">Information</a> </i> &nbsp;
The research presents the methodology of improving the accuracy in sentiment classification in the light of modelling the latent semantic relations (LSR).  ...  of the hierarchical contextually-oriented sentiment dictionary in order to perform the context-sensitive SCP.  ...  Latent Dirichlet Allocation Model Latent Dirichlet Allocation (LDA) is a generative probabilistic graphical model based on a three-level hierarchical Bayesian modelling approach [9, 10, 30] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/info9120307">doi:10.3390/info9120307</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/emmrmvnvmfhz3gwthy5nmrkhyy">fatcat:emmrmvnvmfhz3gwthy5nmrkhyy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190505232940/https://res.mdpi.com/information/information-09-00307/article_deploy/information-09-00307-v2.pdf?filename=&amp;attachment=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/57/bd/57bd632d8c1e401908598bf8f57d35eff1e04a9a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/info9120307"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>
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