The Accuracy Comparison Between Word2Vec and FastText On
Sentiment Analysis of Hotel Reviews

The Accuracy Comparison Between Word2Vec and FastText On
Sentiment Analysis of Hotel Reviews

word2vec, fast text, sentiment analysis, hotel review

Word embedding vectorization is more efficient than Bag-of-Word in word vector size. Word embedding also overcomes the
loss of information related to sentence context, word order, and semantic relationships between words in sentences. Several
kinds of Word Embedding are often considered for sentiment analysis, such as Word2Vec and FastText. Fast Text works on NGram, while Word2Vec is based on the word. This research aims to compare the accuracy of the sentiment analysis model
using Word2Vec and FastText. Both models are tested in the sentiment analysis of Indonesian hotel reviews using the dataset
from TripAdvisor.Word2Vec and FastText use the Skip-gram model. Both methods use the same parameters: number of
features, minimum word count, number of parallel threads, and the context window size. Those vectorizers are combined by
ensemble learning: Random Forest, Extra Tree, and AdaBoost. The Decision Tree is used as a baseline for measuring the
performance of both models. The results showed that both FastText and Word2Vec well-to-do increase accuracy on Random
Forest and Extra Tree. FastText reached higher accuracy than Word2Vec when using Extra Tree and Random Forest as
classifiers. FastText leverage accuracy 8% (baseline: Decision Tree 85%), it is proofed by the accuracy of 93%, with 100
estimators

Siti Khomsah1
, Rima Dias Ramadhani2
, Sena Wijayanto3

Data Science,
3
Information Systems, Faculty of Informatics, Telkom Institute of Technology Purwokerto

: 30-06-2022

Fajr Bagus W

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Indonesia

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