Word2Vec for Indonesian Sentiment Analysis towards Hotel Reviews: An Evaluation Study

Nawangsari, Rizka Putri; Kusumaningrum, Retno; Wibowo, Adi

doi:10.1016/j.procs.2019.08.178

Cited by 55 publications

(32 citation statements)

References 6 publications

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“…This sentiment lexicon is built for the hospitality domain to increase accuracy than Bing Liu's dictionary for all different domain (Hu & Liu, 2004;Q. Li et al, 2019;Nawangsari et al, 2019). The study also performed 02 measurement tasks, including: (1) measuring satisfaction of guest for overall Vietnamese hotels; (2) measuring satisfaction of guest on 06 criteria.…”

Section: Public Interest Statementmentioning

confidence: 99%

Measuring guest satisfaction from online reviews: Evidence in Vietnam

Thu

2020

Cogent Social Sciences

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Mining of guest's online reviews is a new trend study that helps managers understand customers' feeling with services they provide. The satisfaction or dissatisfaction of guest who experienced very important to manager. Hotel managers can be improve the quality of the service through reviews of guest. In this study, the authors present a solution to measure guest satisfaction about Vietnamese hotels through online reviews. Natural language processing technology and machine learning were used to analyze the satisfaction of guest through 06 criteria including: staff, food, room, sevices, location and value. With the data crawled from TripAdvisor, Vietnamese hotel reached up to 72% on average for all criteria and the most satisfactory was the staff reached 77.3%.

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Section: Public Interest Statementmentioning

confidence: 99%

Measuring guest satisfaction from online reviews: Evidence in Vietnam

Thu

2020

Cogent Social Sciences

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“…Word2Vec is an alternative method for generating vector spaces from a corpus. One of the most significant advantages of the Word2Vec model is that it represents characteristics as dense vectors rather than sparse ones that allow it to overcome the problem of synonyms and homonyms, which is common in natural language processing problems [25]. Based on research conducted by [25], for Indonesian reviews, the Word2Vec model for sentiment analysis of hotel reviews shows the best accuracy in the skip-gram model architecture, hierarchical softmax for evaluation methods, and a value of 100 for vector dimensions.…”

Section: Article Historymentioning

confidence: 99%

“…One of the most significant advantages of the Word2Vec model is that it represents characteristics as dense vectors rather than sparse ones that allow it to overcome the problem of synonyms and homonyms, which is common in natural language processing problems [25]. Based on research conducted by [25], for Indonesian reviews, the Word2Vec model for sentiment analysis of hotel reviews shows the best accuracy in the skip-gram model architecture, hierarchical softmax for evaluation methods, and a value of 100 for vector dimensions. Therefore, based on previous research, this study aims to validate the efficacy of a CNN when compared to classical machine learning algorithms, such as LR, NB, and SVM, using the Word2Vec model.…”

Section: Article Historymentioning

confidence: 99%

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Sentiment analysis of Indonesian hotel reviews: from classical machine learning to deep learning

Kusumaningrum

Nisa

Nawangsari

et al. 2021

Int. J. Adv. Intell. Informatics

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Currently, there are a large number of hotel reviews on the Internet that need to be evaluated to turn the data into practicable information. Deep learning has excellent capabilities for recognizing this type of data. With the advances in deep learning paradigms, many algorithms have been developed that can be used in sentiment analysis tasks. In this study, we aim to compare the performance of classical machine learning algorithms—logistic regression (LR), naïve Bayes (NB), and support vector machine (SVM) using the Word2Vec model in conjunction with deep learning algorithms such as a convolutional neural network (CNN) to classify hotel reviews on the Traveloka website into positive or negative classes. Both learning methods apply hyperparameter tuning to determine the parameters that produce the best model. Furthermore, the Word2Vec model parameters use the skip-gram model, hierarchical softmax evaluation, and the value of 100 vector dimensions. The highest average accuracy obtained was 98.08% by using the CNN with a dropout of 0.2, Tanh as convolution activation, softmax as output activation, and Adam as the optimizer. The findings from the study demonstrate that the integration of the Word2Vec model and the CNN model obtains significantly better accuracy than other classical machine learning methods.

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“…The Word2Vec model can process unstructured text data by taking a corpus of words as input and generating a word vector. One of the main advantages of the Word2Vec model is that it represents features as dense vectors rather than conventional tenuous representations, which are generally able to solve the synonym and homonym problems that are often encountered in NLP tasks so that this method produces an accuracy of 89% [12].…”

Section: Introductionmentioning

confidence: 99%

Prediction Meaning of Words with Word2Vec on Whatsapp Data for Disaster Topic

2021

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The community’s need for conditions that are safe from disasters is the basis for the problems of the system to be created. The system that will be created is able to detect the meaning of words from Whatsapp data obtained from the public. The data used is Whatsapp social media data, systematics of data processing starts from pre-processing and ends with an extended factorization matrix Word2Vec analysis, which is called Continuous Bag-of-Word (CBOW) to get the meaning of sentences as early detection of disaster locations. The system can extract 43% required Whatsapp data in the total of data processed by the system, and total accuracy on Word2Vec is 79%.

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Word2Vec for Indonesian Sentiment Analysis towards Hotel Reviews: An Evaluation Study

Cited by 55 publications

References 6 publications

Measuring guest satisfaction from online reviews: Evidence in Vietnam

Measuring guest satisfaction from online reviews: Evidence in Vietnam

Sentiment analysis of Indonesian hotel reviews: from classical machine learning to deep learning

Prediction Meaning of Words with Word2Vec on Whatsapp Data for Disaster Topic

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