Web-sentiment analysis of public comments (public reviews) for languages with limited resources such as the K azakh language (2021)

Abstract: In the pandemic period, the stay-at-home trend forced businesses to switch their activities to digital mode, for example, app-based payment methods, social distancing via social media platforms, and other digital means have become an integral part of our lives. Sentiment analysis of textual information in user comments is a topical task in emotion AI because user comments or reviews are not homogeneous, they contain sparse context behind, and are misleading both for human and computer. Barriers arise from the emotional language enriched with slang, peculiar spelling, transliteration, use of emoji and their symbolic counterparts, and code-switching. For low resource languages sentiment analysis has not been worked upon extensively, because of an absence of ready-made tools and linguistic resources for sentiment analysis. This research focuses on developing a method for aspect-based sentiment analysis for Kazakh-language reviews in Android Google Play Market.

## introduction top free google play apps in kazakhstan are ashyq, telegram messenger, kaspi, instagram, e-gov mobile, olx, kolesa, and others. kazakh-language application reviews are of high interest for companies developing and marketing mobile apps. they are valued by businesses to influence the decision-making of a potential customer and evaluate overall satisfaction or dissatisfaction with products, their new features, and services. the given problem predetermines the following obstacle: in the context of the absence of a standard dataset, comparison of techniques applied, and results obtained is a challenging task. the problem of sentiment classification of kazakh comments includes code-switching between kazakh and russian languages by using cyrillic script, ambiguous words, and emoji, especially the problem of combining negative emoji with a positive comment or vice versa, which have an impact on the overall tonality of the review. the relevance of the topic is justified by the scarcity of developed models and methods of sentiment analysis of the opinions of social networks users in the kazakh language. collection of 3000 labeled reviews data set on web apps and development of a method for achieving more correct results is the main goal, which consists of data compilation, pre-processing part, and classification by testing an aspect-based method. ## previous work researchers in the area of sentiment analysis and opinion mining have achieved significant progress. according to @xcite , sentiment is a subjective feeling expressed in text. @xcite stated that analysis of subjective feelings in texts refers to the assemble of techniques from fields such as natural language processing (nlp), information retrieval, and machine learning, to identify and extract subjective information from text reviews ## methodology this research focuses on the development of a new method for aspect-based sentiment analysis for kazakh-language reviews of android apps posted on the google play market. the research questions set during the research planning are: the relevance of the research topic is justified by the scarcity of developed models and methods of sentiment analysis of the opinions of social networks users in the kazakh language. collection of 3000 labeled reviews data set on web apps and development of a method for achieving more correct results is a contribution and can be used for future research. the object of the research is the user reviews from android google play market in the kazakh language. the subject of the research is sentiment analysis methods and algorithms, as a special case of semantic analysis of texts in natural language. the main steps of sentiment analysis are data compilation and preprocessing, as well as classifying it with the use of machine learning algorithms and neural networks. the first stage contains an accumulation of kazakh-language reviews of android apps posted on the google play market. classification consisted of labeled data such as positive, negative, neutral labels based on my personal standpoint, reviews containing only emoji (without words), emoji and words, reviews with code-switching, google play market ratings (1-5 stars), binary classification by google play market. the compiled data consists of 3000 annotated tokens, which can be used for training and testing. the second stage includes data preprocessing that implies clearing data from unneeded characters and punctuation marks, converting all words to lower case, extracting stop words that do not possess any semantic load, and removing affixes and endings (stemming), or modifying words into an indefinite form (lemmatization). up to far tokenization was performed with the use of python, reduced all tokens to lower case, added notes containing corrected tokens for misspelled tokens, which refers to any type of data created by users, and far from standards of literary language. national laboratory of kazakhstan contributed to developing the kaznlp tool for text preprocessing, and nla-nu laboratories developed kazakh dependency treebank for parsing. in the third stage, it will be tested whether a rule-based approach for identifying different aspects of apps mentioned in the reviews, in the context of scarce data, is more accurate than a data-driven approach. ## current progress at the current stage, 3000 kazakh comments are compiled. they were labeled manually according to emotional polarity and compared with pre-defined sentiment classification on google play market, the second stage was followed by defining code-switching, peculiar misspelling, and emoji identification. at the third stage, there is an ongoing process of text pre-processing. user comments were tokenized, lemmatized, and keywords were extracted in sketch engine to define a list of aspects. the next step will be processed in order to define the effective way of applying machine learning tasks for sentiment extraction.