Towards the Uzbek Language Endings as a Language Resource

Matlatipov, Sanatbek; Tukeyev, Ualsher; Арипов, Мерсаид

doi:10.1007/978-3-030-63119-2_59

Cited by 8 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The technique is based on [8] paper where it is a proposed algorithm of automatic detection of single word stop words collection using TFIDF(Term frequencyinverse document frequency). After that, each word is processed to lexicon-free stemming tool [7] algorithm for decreasing the word capacity because of prefixes and suffixes. The basic idea is using the combinatorial approach of eligible endings candidates.…”

Section: Data Pre-processingmentioning

confidence: 99%

“…For example, the language we focus on is the Uzbek language which is being used by around 34 million native speakers in Uzbekistan and elsewhere in Central Asia and China 3 . Uzbek is a null-subject and highly agglutinative language where one word can be a meaningful sentence [6,7]. To our knowledge, there is no previous work for sentiment classification problems based on restaurant domain feedback.…”

Section: Introductionmentioning

confidence: 99%

“…The first steps are removing URLs, punctuation, and lower-casing. The second step is ignoring stopwords [8] from the dataset where it is based on accuracy evaluation after generating the list of stop words using the TF-IDF algorithm; Then, we applied the stemming algorithm [7,9] which is based on Uzbek words' endings' electronic dictionary that uses combinatorial approach inferring apply for part of speech of the Uzbek language: nouns, adjectives, numerals, verbs, participles, moods, voices. Advantages of using the algorithm are lexicon-free and its complexity that allows one operation (referring to the dictionary of endings of the language) to perform: segmentation of the word into suffixes; performs morphological analysis of the word.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Uzbek Sentiment Analysis based on local Restaurant Reviews

Matlatipov¹,

Rahimboeva²,

Rajabov³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Data Pre-processingmentioning

confidence: 99%