SentiLangN: A Language-Neutral Graph-Based Approach for Sentiment Analysis in Microblogging Data

Abulaish, Muhammad; Rahimi, Mohammad Baqir; Ebrahemi, Habeebullah; Sah, Amit Kumar

doi:10.1145/3350546.3352568

Cited by 2 publications

(1 citation statement)

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“…Foreign scholars have studied methods of intellectual text analysis. For instance, Abulaish et al (2019) present a language-independent approach to graph-based tonality analysis, SentiLangN, which uses a symbolic n-gram graph to model text data for processing language-independent unstructured expressions. Hadi et al (2019) offer an effective technique for analyzing tonality in the context of big data.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Text Mining in Socio-Economic Research

Malyshenko,

Anashkina

et al. 2024

International Journal of Business Data Communications and Networking

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This work aims to analyze insights from social networks for identification of population satisfaction with pay level in Russia using the text mining approach. For this, a sentiment analysis framework was developed, which integrates Twitter mining tools and a sentiment index. Sentiments were extracted using Twitter mining and then recoded and substituted into the sentiment formula. The results of sentiment analysis indicate low satisfaction with levels of pay among Russians. Twitter was chosen as the object of research, as one of the most active and independent networks in Russia. It is possible that some of the tweets belong to authors who are not living in Russia at the moment, but their number is not significant and their interest in this issue, in the authors' opinion, only enhances the relevance of the problem under study.

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Section: Literature Reviewmentioning

confidence: 99%