# Uzbek Sentiment Analysis based on local Restaurant Reviews

arXiv: https://arxiv.org/abs/2205.15930 · HTML: https://mlsanity.com/paper/2205.15930/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 31 May 2022.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1510.03820/) (2015)
- [Cross-Lingual Word Embeddings for Turkic Languages](https://mlsanity.com/paper/2005.08340/) (2020)
- [Does Corpus Quality Really Matter for Low-Resource Languages?](https://mlsanity.com/paper/2203.08111/) (2022)
- [SimRelUz: Similarity and Relatedness scores as a Semantic Evaluation dataset for Uzbek language](https://mlsanity.com/paper/2205.06072/) (2022)
- [Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets](https://mlsanity.com/paper/1709.04219/) (2017)
- [A machine transliteration tool between Uzbek alphabets](https://mlsanity.com/paper/2205.09578/) (2022)
- [Investigating the Effect of Emoji in Opinion Classification of Uzbek Movie Review Comments](https://mlsanity.com/paper/2008.00482/) (2020)
- [UzBERT: pretraining a BERT model for Uzbek](https://mlsanity.com/paper/2108.09814/) (2021)

## Built on by (most cited first, 3 of 3)

- [Recent Advancements and Challenges of Turkic Central Asian Language Processing](https://mlsanity.com/paper/2407.05006/) (2024)
- [Text classification dataset and analysis for Uzbek language](https://mlsanity.com/paper/2302.14494/) (2023)
- [UzbekTagger: The rule-based POS tagger for Uzbek language](https://mlsanity.com/paper/2301.12711/) (2023)

## Abstract (arXiv)

Extracting useful information for sentiment analysis and classification problems from a big amount of user-generated feedback, such as restaurant reviews, is a crucial task of natural language processing, which is not only for customer satisfaction where it can give personalized services, but can also influence the further development of a company. In this paper, we present a work done on collecting restaurant reviews data as a sentiment analysis dataset for the Uzbek language, a member of the Turkic family which is heavily affected by the low-resource constraint, and provide some further analysis of the novel dataset by evaluation using different techniques, from logistic regression based models, to support vector machines, and even deep learning models, such as recurrent neural networks, as well as convolutional neural networks. The paper includes detailed information on how the data was collected, how it was pre-processed for better quality optimization, as well as experimental setups for the evaluation process. The overall evaluation results indicate that by performing pre-processing steps, such as stemming for agglutinative languages, the system yields better results, eventually achieving 91% accuracy result in the best performing model