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<p>This paper presents a novel topic modeling approach, Semantic Sentimental LDA (SSLDA), designed to improve sentiment detection in short texts by leveraging prior sentiment knowledge from reliable sources. Specifically, we utilize emojis extracted from a dataset of approximately 8 million tweets to refine sentiment classification. The methodology follows a structured 9-stage process, where sentiment-bearing words identified in earlier stages are progressively integrated into subsequent steps to enhance classification robustness. Rather than employing traditional supervised methods, our approach iteratively refines a sentiment lexicon, termed the Golden List, which distinguishes sentiment-positive and sentiment-negative words—including previously unrecognized terms used informally in microblogging contexts. Comparative analysis against existing sentiment lexicons demonstrates a higher rate of sentiment alignment, validating the effectiveness of SSLDA in addressing the challenges of informal text sentiment classification.</p>

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Semantic sentiment based LDA for detecting sentiments of short texts

  • 期刊论文

  • 发表日期：

    2025年9月19日

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  • Mir Saman Tajbakhsh,
  • Vahid Solouk,
  • Vahid Ranjbar,
  • Mostafa Salehi

摘要

This paper presents a novel topic modeling approach, Semantic Sentimental LDA (SSLDA), designed to improve sentiment detection in short texts by leveraging prior sentiment knowledge from reliable sources. Specifically, we utilize emojis extracted from a dataset of approximately 8 million tweets to refine sentiment classification. The methodology follows a structured 9-stage process, where sentiment-bearing words identified in earlier stages are progressively integrated into subsequent steps to enhance classification robustness. Rather than employing traditional supervised methods, our approach iteratively refines a sentiment lexicon, termed the Golden List, which distinguishes sentiment-positive and sentiment-negative words—including previously unrecognized terms used informally in microblogging contexts. Comparative analysis against existing sentiment lexicons demonstrates a higher rate of sentiment alignment, validating the effectiveness of SSLDA in addressing the challenges of informal text sentiment classification.