Opinion Mining from Social Travel Networks

Puri, Charu; Yadav, Akhil; Jangra, Gaurav; Saini, Kush; Kumar, Naveen

doi:10.1007/978-3-319-65139-2_8

Cited by 2 publications

(1 citation statement)

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“…Vector Machine (SVM) was applied to automate the online review classification in the work [7] and achieved an accuracy of 82.7%. A SentiWordNet based model was proposed by the authors in [9] and got 87% accuracy in classifying the positive and negative reviews from hotel reviews. Visual analytics along with a multi-feature fusion CNN model can also be applied to classify the customers' responses.…”

Section: Related Workmentioning

confidence: 99%

Towards Mining Public Opinion: An Attention-Based Long Short Term Memory Network Using Transfer Learning

Hossain,

Rashid,

Islam

et al. 2022

JCC

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The Internet provides a large number of tools and resources, such as social media sites, online newsgroups, blogs, electronic forums, virtual communities, and online travel sites, for consumers to express their views or opinions regarding various issues. These opinions can help organizations like tourism to improve their products and services for their consumers. Opinion mining refers to a process of identifying emotions by applying Natural Language Processing (NLP) techniques to a pool of texts. This paper mainly focuses on mining public opinion from the hotel reviews domain. To do so, we proposed a novel technique called the Attention-Based Long Short Term Memory (Attention-LSTM) Network using a transfer learning approach. We empirically analyzed several machine learning and deep learning methods and observed our proposed technique provided an adequate performance for mining public opinion in the hotel reviews domain.

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Section: Related Workmentioning

confidence: 99%