Adapting sentiment analysis system from English to Slovak

Mikula, Martin; Gao, Xiaoying; Machová, Kristína

doi:10.1109/ssci.2017.8285313

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…The same dataset was also used in the study by Mikula et al [33]. Their approach involved an automated annotation of a dictionary derived from English, utilizing Particle Swarm Optimization (PSO) and Bare-bones Particle Swarm Optimization (BBPSO) to assign polarity values to the dictionary words.…”

Section: Related Work Focused On Slovak Languagementioning

confidence: 99%

Comparison of Machine Learning Approaches for Sentiment Analysis in Slovak

Sokolová,

Harahus,

Juhár

et al. 2024

Electronics

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The process of determining and understanding the emotional tone expressed in a text, with a focus on textual data, is referred to as sentiment analysis. This analysis facilitates the identification of whether the overall sentiment is positive, negative, or neutral. Sentiment analysis on social networks seeks valuable insight into public opinions, trends, and user sentiments. The main motivation is to enable informed decisions and an understanding of the dynamics of online discourse by businesses and researchers. Additionally, sentiment analysis plays a vital role in the field of hate speech detection, aiding in the identification and mitigation of harmful content on social networks. In this paper, studies on the sentiment analysis of texts in the Slovak language, as well as in other languages, are introduced. The primary aim of the paper, aside from releasing the “SentiSK” dataset to the public, is to evaluate our dataset by comparing its results with those of other existing datasets in the Slovak language. The “SentiSK” dataset, consisting of 34,006 comments, was created, specified, and annotated for the task of sentiment analysis. The proposed approach involved the utilization of three datasets in the Slovak language, with nine classification methods trained and compared in two defined tasks. For the first task, testing on the “SentiSK” and “Sentigrade” datasets involved three classes (positive, neutral, and negative). In the second task, testing on the “SentiSK”, “Sentigrade”, and “Slovak dataset for SA” datasets involved two classes (positive and negative). Selected models achieved an F1 score ranging from 75.35% to 95.04%.

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Section: Related Work Focused On Slovak Languagementioning

confidence: 99%

Comparison of Machine Learning Approaches for Sentiment Analysis in Slovak

Sokolová,

Harahus,

Juhár

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

Random Regrouping and Factorization in Cooperative Particle Swarm Optimization Based Large-Scale Neural Network Training

Dennis

Ombuki-Berman

Engelbrecht

2019

Neural Process Lett

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A Systematic Review of Cross-Lingual Sentiment Analysis: Tasks, Strategies, and Prospects

Zhao,

Wu,

Yang

et al. 2024

ACM Comput. Surv.

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Traditional methods for sentiment analysis, when applied in a monolingual context, often yield less than optimal results in multilingual settings. This underscores the need for a more thorough exploration of cross-lingual sentiment analysis (CLSA) methodologies to improve analytical effectiveness. CLSA, confronted with obstacles such as linguistic disparities and a lack of resources, seeks to evaluate sentiments across a range of languages. First, the research background, challenges, existing solution ideas and evaluation tasks of CLSA are summarized. Subsequently, new perspectives including different granularity levels, machine translation support, and sentiment transfer strategies perspectives are highlighted. Finally, potential avenues for future research are discussed.

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Adapting sentiment analysis system from English to Slovak

Cited by 4 publications

References 19 publications

Comparison of Machine Learning Approaches for Sentiment Analysis in Slovak

Comparison of Machine Learning Approaches for Sentiment Analysis in Slovak

Random Regrouping and Factorization in Cooperative Particle Swarm Optimization Based Large-Scale Neural Network Training

A Systematic Review of Cross-Lingual Sentiment Analysis: Tasks, Strategies, and Prospects

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