uzbek-sentiment-analysis

Sentiment analysis in Uzbek language and new Datasets of Uzbek App reviews for Sentiment Classification

Feel free to use the dataset and the tools presented in this project, a paper about more details on creation and usage here.

If you find it useful, plese make sure to cite the paper:

@inproceedings{DBLP:conf/ltconf/KuriyozovMAG19,
  author    = {Elmurod Kuriyozov and
               Sanatbek Matlatipov and
               Miguel A. Alonso and
               Carlos G{\'{o}}mez{-}Rodr{\'{\i}}guez},
  editor    = {Zygmunt Vetulani and
               Patrick Paroubek and
               Marek Kubis},
  title     = {Construction and Evaluation of Sentiment Datasets for Low-Resource
               Languages: The Case of Uzbek},
  booktitle = {Human Language Technology. Challenges for Computer Science and Linguistics
               - 9th Language and Technology Conference, {LTC} 2019, Poznan, Poland,
               May 17-19, 2019, Revised Selected Papers},
  series    = {Lecture Notes in Computer Science},
  volume    = {13212},
  pages     = {232--243},
  publisher = {Springer},
  year      = {2019},
  url       = {https://doi.org/10.1007/978-3-031-05328-3\_15},
  doi       = {10.1007/978-3-031-05328-3\_15},
  timestamp = {Thu, 23 Jun 2022 19:58:44 +0200},
  biburl    = {https://dblp.org/rec/conf/ltconf/KuriyozovMAG19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

The main contributions of this project are:

1. The creation of the first annotated dataset for sentiment analysis in Uzbek language, obtained from reviews of the top 100 Google Play Store applications used in Uzbekistan. This manually annotated dataset contains 2500 positive and 1800 negative reviews. Furthermore, we have also built a larger dataset by automatically translating (using Google Translate API) an existing English dataset of application reviews. The translated dataset has≈10K positive and≈10K negative app reviews, after manually eliminating themajor machine translation errors by either correctingor removing them completely.

2. The definition of the baselines for sentiment analyses in Uzbek by considering both traditional machine learning methods as well as recent deep learning techniques fed with fastText pre-trained word embeddings. Although all the tested models are relatively accurate and differences between models are small, the neural network models tested do not manage tosubstantially outperform traditional models. We believe that the quality of currently available pre-trained word embeddings for Uzbek is not enough to let deep learning models perform at their full potential.

The results obtained through the research: Table: Accuracy results with different training and test sets.ManualTT- Manually annotated Training and Test sets.TransTT- Translated Training and Test sets.TTMT- Translated dataset for Training, Annotated dataset for Test set.