Summary of Decoding Multilingual Topic Dynamics and Trend Identification Through Arima Time Series Analysis on Social Networks: a Novel Data Translation Framework Enhanced by Lda/hdp Models, By Samawel Jaballi et al.
Decoding Multilingual Topic Dynamics and Trend Identification through ARIMA Time Series Analysis on Social Networks: A Novel Data Translation Framework Enhanced by LDA/HDP Models
by Samawel Jaballi, Azer Mahjoubi, Manar Joundy Hazar, Salah Zrigui, Henri Nicolas, Mounir Zrigui
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The authors present a novel methodology for decoding multilingual topic dynamics and identifying communication trends during crises. They focus on dialogues within Tunisian social networks during the Coronavirus Pandemic and other notable themes like sports and politics. The approach involves aggregating a varied multilingual corpus of comments, refining the dataset through preprocessing, and introducing No-English-to-English Machine Translation to handle linguistic differences. Empirical tests show high accuracy and F1 scores for this method, highlighting its suitability for linguistically coherent tasks. Advanced modeling techniques like LDA and HDP models are employed to extract pertinent topics from translated content, allowing ARIMA time series analysis to decode evolving topic trends. The authors apply their method to a multilingual Tunisian dataset, effectively identifying key topics mirroring public sentiment. These insights prove vital for organizations and governments striving to understand public perspectives during crises. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how to understand what people are talking about on social media during big events like the coronavirus pandemic. The researchers looked at conversations in Tunisia that mentioned sports, politics, and the pandemic. They used a special way of translating words from different languages into English to help them understand what was being said. Then they used some advanced math tools to figure out what topics were most important and how they changed over time. By doing this, they were able to identify key themes that reflected public opinion. This kind of information can be really helpful for organizations and governments trying to understand people’s perspectives during times of crisis. |
Keywords
* Artificial intelligence * Time series * Translation
