Sentiment analysis for reviews of restaurants in Myanmar text

Aye, Yu Mon; Aung, Sint Sint

doi:10.1109/snpd.2017.8022740

Cited by 15 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed method outperforms the previous studies in terms of accuracy. Aye et al [24] perform SA on the Myanmar language using a lexicon-based approach using a dataset of 500 restaurant reviews collected from OSNs. The authors develop a lexicon using a dictionarybased approach.…”

Section: A Lexicon-based Approachmentioning

confidence: 99%

Deep Learning Based Cross Domain Sentiment Classification for Urdu Language

Altaf

Jamal

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Sentiment analysis is a widely researched area due to its various applications in customer services, brand monitoring, and market research. Automatic sentiment classification is an important but challenging task. Contrary to the English language, sentiment analysis for low-resource languages like Urdu is an under-explored research area. Most of the work on sentiment analysis in the Urdu language is domain-dependent where models are mostly trained and tested on the same dataset on limited domains. However, sentiments in different domains are expressed differently, and manually annotating the datasets for all possible domains is unfeasible. Training a sentiment classifier using annotated data on one domain and testing it on another domain results in poor performance as the terms appearing in the source domain (training data) might not appear in the target (testing data) domain. In this paper, we present a baseline method for cross-domain sentiment analysis in the Urdu language using two different domains. Feature extraction is performed using n-grams and word embedding techniques. Sentiment classification is performed using machine learning and deep learning classifiers. The proposed method achieves an accuracy, precision, recall, and F1 scores of 0.77, 0.83, 0.68, and 0.75, respectively.INDEX TERMS Cross-domain sentiment analysis; deep learning; Urdu language processing; feature engineering

show abstract

Section: A Lexicon-based Approachmentioning

confidence: 99%

Deep Learning Based Cross Domain Sentiment Classification for Urdu Language

Altaf

Jamal

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The traditional sample disequilibrium processing method includes an oversampling method, undersampling method, and reweighting method [ 22 , 23 , 24 ], among which the traditional oversampling and undersampling method is to copy and delete the original sample. However, this method of obtaining text features causes repetition and waste of data features and cannot play a great role in the learning of fine-grained emotion features, while the multi-modal reweighting method also has problems more or less [ 25 , 26 , 27 ]. Therefore, this paper proposes a SOM oversampling method - an oversampling method based on TF-IDF synonymous substitution.…”

Section: Data Preprocessing and Model Architecturementioning

confidence: 99%

Multimodal Feature Fusion Method for Unbalanced Sample Data in Social Network Public Opinion

Zhao

Dong

Shi

et al. 2022

Sensors

View full text Add to dashboard Cite

With the wide application of social media, public opinion analysis in social networks has been unable to be met through text alone because the existing public opinion information includes data information of various modalities, such as voice, text, and facial expressions. Therefore multi-modal emotion analysis is the current focus of public opinion analysis. In addition, multi-modal emotion recognition of speech is an important factor restricting the multi-modal emotion analysis. In this paper, the emotion feature retrieval method for speech is firstly explored and the processing method of sample disequilibrium data is then analyzed. By comparing and studying the different feature fusion methods of text and speech, respectively, the multi-modal feature fusion method for sample disequilibrium data is proposed to realize multi-modal emotion recognition. Experiments are performed using two publicly available datasets (IEMOCAP and MELD), which shows that processing multi-modality data through this method can obtain good fine-grained emotion recognition results, laying a foundation for subsequent social public opinion analysis.

show abstract

“…They used tweets data. In paper [4], Y. M. Aye and S .S. Aung developed the Myanmar sentiment lexicon of food and restaurant domain and analyze the sentiment of Myanmar text customer reviews for recommendation.…”

Section: Related Workmentioning

confidence: 99%

Myanmar News Sentiment Analyzer using Support Vector Machine Algorithm

Yu¹

2019

IJATCSE

View full text Add to dashboard Cite

Sentiment analysis is one of the natural language processing fields that combined computational linguistics and information retrieval. It is one of text classification techniques that extract opinion expressed in a positive and negative. The news, blogs and reviews obtained from social network are become important resources for sentiment analysis. This paper implements sentiment analyzer for Myanmar news. This system create sentiment annotated corpus for Myanmar news that are collected from Myanmar news web sites and ALT Treebank. Feature extraction and transformation are also needed to extract feature and transform to feature vector to get high performance. N-gram and TFIDF weighting methods are used in this system. Machine learning is combination of the techniques and basis from both statistics and computer science. This system is implemented by using machine learning algorithm Support Vector Machine compared with Logistics Regression and Naïve Bayes algorithms. We showed the comparison results of those algorithms and also showed that SVM is the more powerful than other two algorithms. User can easily know how importance about news by using this sentiment analyzer system.

show abstract

Sentiment analysis for reviews of restaurants in Myanmar text

Cited by 15 publications

References 4 publications

Deep Learning Based Cross Domain Sentiment Classification for Urdu Language

Deep Learning Based Cross Domain Sentiment Classification for Urdu Language

Multimodal Feature Fusion Method for Unbalanced Sample Data in Social Network Public Opinion

Myanmar News Sentiment Analyzer using Support Vector Machine Algorithm

Contact Info

Product

Resources

About