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Abdulaziz Almaslukh1, Aisha Almaalwy1, Nasser Allheeib1

  • 1Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.

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Summary

This study introduces an efficient framework for analyzing social media sentiment, speeding up searches for location-based, time-sensitive public opinion on platforms like X (formerly Twitter). The new query method improves search times tenfold.

Keywords:
Geo-textualSentiment analysisSpatial indexTemporal data

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Area of Science:

  • Social Media Analysis
  • Natural Language Processing
  • Data Mining

Background:

  • Social media platforms like X (formerly Twitter) generate vast amounts of data suitable for sentiment analysis.
  • Existing sentiment analysis often incorporates spatial and temporal dimensions for precision.
  • A need exists for faster, location-specific sentiment analysis of recent social media posts.

Purpose of the Study:

  • To develop a general framework for data indexing and search queries to simplify and accelerate sentiment analysis of social media data.
  • To enhance spatial-temporal data analysis with sentiment classification.
  • To enable efficient retrieval of recent top-k tweets based on location and sentiment.

Main Methods:

  • Proposed a novel search query extending fundamental spatial distance queries.
  • Integrated sentiment analysis to classify temporal data as positive, negative, or neutral.
  • Operated the query on an indexed dataset for efficient processing.

Main Results:

  • The proposed query demonstrated over a tenfold improvement in query time compared to baseline methods.
  • Performance was evaluated across various parameters including top-k, query distance, and keyword count.
  • The framework effectively simplifies the search process for location-based sentiment analysis.

Conclusions:

  • The developed framework significantly enhances the efficiency of searching and analyzing sentiment in spatial-temporal social media data.
  • This approach facilitates a better understanding of public opinion by providing faster access to relevant, localized sentiment information.
  • The query extension offers a scalable solution for real-time sentiment analysis on large social media datasets.