Myanmar Lexicon Based Sentiment Analysis on Hotel Reviews


  • Khaing Su Latt University of Computer Studies, Mandalay, Mandalay-Mogok Road, Patheingyi Township, Mandalay, Myanmar
  • Myo Khaing University of Computer Studies, Mandalay, Mandalay-Mogok Road, Patheingyi Township, Mandalay, Myanmar


Deep Learning, Convolutional Neural Network, Sentiment Analysis, Natural Language Processing, Word2Vector


As social media and digital communication use increases in Myanmar, sentiment analysis is being used more and more in business, politics, and social trends. Big social data analytics is a valuable tool that can be utilized to uncover significant information from social user data. This methodology integrates diverse statistical techniques, sentiment analysis, multimedia administration, and social media analytics to anticipate and predict individuals and examine patterns. Natural Language Processing (NLP) tools and frameworks are becoming more customizable and easily accessible, which makes the process of creating language models unique to Myanmar easier. The proposed system's lexicon will have six categories of aspects (Room, Staff, Facilities, Location, Value, General), together with their corresponding subcategories and opinion terms. After that, word2vec is used to train the reviews of the annotated corpus and create a word embedding model. Because of the nature of the Myanmar language, it is particularly more difficult to perform aspect-level opinion mining on reviews about Myanmar. As a result, the proposed system's primary goal is to employ syntactic patterns and rules to extract pertinent pairs of attributes and opinion terms from user evaluations. The proposed method could be increase the accuracy of sentiment analysis on social media postings written in Myanmar.


M.G.V. Nunes and L. V. Avanc, “Lexicon-based Sentiment Analysis for Reviews of Products in Brazilian Portuguese,”, Conference of Intelligent Systems in Brazilian, 2014.

A. Levchik and N. Loukachevitch, “Creating a General Russian Sentiment Lexicon”, ICLRE conference, 2016.

T. T. Zin and et al., “Domain-Specific Sentiment Lexicon for Classification”, ICAIT, Myanmar, November, 2017.

W. W. Thant and K. Shirai, “Automatic Acquisition of Opinion Words from Myanmar Facebook Movie Comments”, LREC Workshop, Miyazaki, Japan, May 7, 2018.

Y. M. Aye and S. S. Aung, “Senti-Lexicon and Analysis for Restaurant Reviews of Myanmar Text”, IJAEMS, Vol. 4, No 5, May 2018.

C. C. Hnin and N. Naw, “Aspect Level Opinion Mining for Hotel Reviews in Myanmar Language”, IEEE – ICA, July 2018.

T. Yu and K. T. Nwet, “Automatic Sentiment Analysis System for Myanmar News”, ICCA, Myanmar, February, 2019.

W. W. Thant and E. E. Mon, “Analyzing Sentiment of Myanmar Movie Comments Using Naïve Bayes Classifier”, MURC, Yangon, Myanmar, May, 2019.

B. Liu, “Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers”, 2012.

F. Frasincar and K. Schouten “Survey on Aspect-Level Sentiment Analysis”, IEEE , January , 2015.

M. S. Phyu and K. T. Nwet, “A Study on a Joint Deep Learning Model for Myanmar Text Classification”, IEEE Conference on Computer Applications, February, 2020.

N. Y. T. Thein and K. M. Soe, “Sentiment Analysis in Myanmar Language using Convolutional LSTM Neural Network”, IJNLC, Vol.10, No.4, August, 2021.

S. Y. Maw and M. A. Khine, “Aspect based Sentiment Analysis for tourism in Myanmar Language using LSTM”, ICCA, Myanmar, February, 2019.




How to Cite

Khaing Su Latt, & Myo Khaing. (2024). Myanmar Lexicon Based Sentiment Analysis on Hotel Reviews. International Journal of Computer (IJC), 50(1), 132–145. Retrieved from