Implementing LDA Topic Modelling Technique to Study User Reviews in Tourism

Extracting valuable information from unstructured text is a significant difficulty in today's culture due to the ever-increasing proliferation of texts, most of which are generated via online social networks in the form of consumer-generated media. Despite this difficulty, natural language proc...

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Veröffentlicht in:	International Conference on Parallel, Distributed and Grid Computing (PDGC ...) S. 357 - 360
Hauptverfasser:	Chauhan, Amit, Mohana, Rajni
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 25.11.2022
Schlagworte:	Aspect Based Sentiment Analysis. Latent Dirichlet Allocation Latent Semantic Allocation Online Social Networks
ISBN:	9781665454001, 1665454008
ISSN:	2573-3079
Online-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	Extracting valuable information from unstructured text is a significant difficulty in today's culture due to the ever-increasing proliferation of texts, most of which are generated via online social networks in the form of consumer-generated media. Despite this difficulty, natural language processing algorithms are being developed rapidly, including topic modelling approaches for discovering latent subjects in text documents like online reviews. As a result, topic modelling methodologies have not grown in favour of the tourist industry yet. So Authors decided to work on a tourism dataset. The results show us the importance of topic keywords concerning the tourism dataset the best perplexity score we get us with the D3 Dataset was -7.9947.
ISBN:	9781665454001 1665454008
ISSN:	2573-3079
DOI:	10.1109/PDGC56933.2022.10053153