IDEAS home Printed from https://ideas.repec.org/a/epw/ejece0/v7y2023i6id19583.html

Evaluation of Machine Learning Approach for Sentiment Analysis using Yelp Dataset

Author

Listed:
  • Mujiono Sadikin
  • Abi Fauzan

Abstract

Due to the abundance of text data representing public opinion, the Sentiment Analysis study is getting more and more important. Various techniques and methods have been proposed to address the issues. One of those techniques is deep learning algorithms which have been used to achieve great results in Natural Language Processing (NLP) applications. Sentiment Analysis is a part of NLP application that extracts emotional information from texts. In this study, we investigate the performance of sequence-based model, i.e., LSTM, compared with multi-layer perceptron Neural Network (NN) to classify the polarity of the text review based on negative or positive. The dataset used in this study is a restaurant review taken from the Yelp website. The dataset is trained using Word2vec word embedding to convert words contained in the dataset into numerical vector representation which is used as the deep learning model input. Based on the experiment results, it is shown that the LSTM model is outperformed compared to the multi-layer NN model. The best accuracy performance provided by LSTM model is 91%, whereas the best accuracy performance of multi-layer NN model is 76%.

Suggested Citation

  • Mujiono Sadikin & Abi Fauzan, 2023. "Evaluation of Machine Learning Approach for Sentiment Analysis using Yelp Dataset," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 7(6), pages 58-64, November.
    • Handle: RePEc:epw:ejece0:v:7:y:2023:i:6:id:19583
    DOI: 10.24018/ejece.2023.7.6.583
    as

    Download full text from publisher

    File URL: https://eu-opensci.org/index.php/ejece/article/view/19583
    File Function: Abstract page
    Download Restriction: no
    File URL: https://eu-opensci.org/index.php/ejece/article/download/19583/11342
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.24018/ejece.2023.7.6.583?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. J. M. Torres & R. M. Aguilar, 2018. "Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation," Complexity, Hindawi, vol. 2018, pages 1-10, April.
    2. C. A. Martín & J. M. Torres & R. M. Aguilar & S. Diaz, 2018. "Using Deep Learning to Predict Sentiments: Case Study in Tourism," Complexity, Hindawi, vol. 2018, pages 1-9, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.
    2. David Flores-Ruiz & Adolfo Elizondo-Salto & María de la O. Barroso-González, 2021. "Using Social Media in Tourist Sentiment Analysis: A Case Study of Andalusia during the Covid-19 Pandemic," Sustainability, MDPI, vol. 13(7), pages 1-19, March.
    3. Csaba Sidor & Branislav Kršák & Ľubomír Štrba & Michal Cehlár & Samer Khouri & Michal Stričík & Jaroslav Dugas & Ján Gajdoš & Barbora Bolechová, 2019. "Can Location-Based Social Media and Online Reservation Services Tell More about Local Accommodation Industries than Open Governmental Data?," Sustainability, MDPI, vol. 11(21), pages 1-21, October.
    4. Zheng Cao & Heng Xu & Brian Sheng-Xian Teo, 2023. "Sentiment of Chinese Tourists towards Malaysia Cultural Heritage Based on Online Travel Reviews," Sustainability, MDPI, vol. 15(4), pages 1-17, February.
    5. Xiaoming Wang & Xinbo Zhao & Jinchang Ren, 2019. "A New Type of Eye Movement Model Based on Recurrent Neural Networks for Simulating the Gaze Behavior of Human Reading," Complexity, Hindawi, vol. 2019, pages 1-12, March.
    6. Lapuz, Mark Chris M., 2023. "The role of local community empowerment in the digital transformation of rural tourism development in the Philippines," Technology in Society, Elsevier, vol. 74(C).
    7. Zhang, Wanqing & Lin, Zi & Liu, Xiaolei, 2022. "Short-term offshore wind power forecasting - A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM)," Renewable Energy, Elsevier, vol. 185(C), pages 611-628.
    8. Sergio Velázquez Medina & José A. Carta & Ulises Portero Ajenjo, 2019. "Performance Sensitivity of a Wind Farm Power Curve Model to Different Signals of the Input Layer of ANNs: Case Studies in the Canary Islands," Complexity, Hindawi, vol. 2019, pages 1-11, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:epw:ejece0:v:7:y:2023:i:6:id:19583. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: support (email available below). General contact details of provider: https://eu-opensci.org/index.php/ejece .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.