INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Topic Modeling is one of the most popular techniques used for text mining in Natural Language Processing. Topic modeling refers to the task of identifying topics that best describes a set of documents. It will classify data based on a particular topic and determine the relationship between tokens. This is done by extracting the patterns of word clusters and frequencies of words in the document. It has enjoyed success in various applications in machine learning, natural language processing (NLP), and data mining for almost two decades. There are several algorithms for implementing topic modeling. Most common techniques are LDA – Latent Dirichlet Allocation, LSA or LSI – Latent Semantic Analysis or Latent Semantic Indexing. In this paper, we have proposed the Word Embedding Topic Evaluation methodology which will help in identifying the efficient outcomes with better accuracy. It outperforms existing document models that are generally used in measuring topic evaluation such as coherence score, perplexity etc., in terms of topic quality and predictive performance.
Keywords:
Topic Modelling, Evaluation of Topic Modeling, Word Embedding, Word2vec
Cite Article:
"Embedding for Evaluation of Topic Modeling - Unsupervised Algorithms", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 2, page no.110 - 116, February-2022, Available :http://www.ijsdr.org/papers/IJSDR2202018.pdf
Downloads:
000337078
Publication Details:
Published Paper ID: IJSDR2202018
Registration ID:193969
Published In: Volume 7 Issue 2, February-2022
DOI (Digital Object Identifier):
Page No: 110 - 116
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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