Embedding for Evaluation of Topic Modeling - Unsupervised Algorithms
Ms. Ananya Srivastava
, Ms.Lavanya Gunasekar , Mrs. Bagya Lakshmi V
Topic Modelling, Evaluation of Topic Modeling, Word Embedding, Word2vec
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.
"Embedding for Evaluation of Topic Modeling - Unsupervised Algorithms", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 2, page no.110 - 116, February-2022, Available :https://ijsdr.org/papers/IJSDR2202018.pdf
Volume 7
Issue 2,
February-2022
Pages : 110 - 116
Paper Reg. ID: IJSDR_193969
Published Paper Id: IJSDR2202018
Downloads: 000347259
Research Area: Engineering
Country: Chennai , Tamil Nadu, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
