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Sentiment Diversification with Different Biases

This paper explores advanced methods for sentiment diversification in search results, presenting various frameworks to address the bias in opinionated content. Building upon previous work that primarily focused on topical diversity, we propose models to diversify sentiment representation based on three key biases: equal diversification, diversification towards the majority sentiment, and diversification against it. Through experiments, we demonstrate how our approach not only enhances topical variety but also allows the retrieval of a balanced view, taking into account the strength and frequency of sentiments.

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Sentiment Diversification with Different Biases

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  1. Sentiment Diversification with Different Biases Date：2014/04/29 Source：SIGIR’13 Advisor：Prof.Jia-Ling,Koh Speaker：Wei,Chang

  2. Outline • Introduction • Approach • Framework • Biases • Experiment • Conclusion

  3. Introduction • In previous work diversification has mainly been applied for better topical variety in search results • How can opinionated content exhibiting sentiments be diversified?

  4. Sentiments • Search for “global warming” • Negative : concern about it and its effects on the environment • Positive : claim that worries about global climate change are unjustified • Neutral : It's a serious problem but we're handling it"

  5. 3 biases are emphasized in search results • Equal diversification by preferring all sentiments equally. • Diversification towardsthe Topic Sentiment, in which the resulting reranked list mirrors the actual sentiment bias in a topic. • Diversification againstthe Topic Sentiment, in which documents about the minority sentiment(s) are boosted whereas those with the majority sentiment are demoted.

  6. Goal • Propose different diversification models for sentiment diversity with 3 biases.

  7. Outline • Introduction • Approach • Frameworks • Retrieval-Interpolated Diversification • Biases • Equal Sentiment Diversification (BAL) • Diversifying Towards the Topic Sentiment (CRD) • Diversifying Against the Topic Sentiment (OTL) • Diversity by Proportionality • Experiment • Conclusion

  8. Retrieval-Interpolated Diversification • similar to xQuAD : retrieval contribution Q: query D: a document S: selected documents R: retrieved documents : sentiment contribution

  9. Sentiment Contribution by Strength (SCS)

  10. Sentiment Contribution by Strength and Frequency (SCSF)

  11. Favoring Different Biases in Search Results • Equal Sentiment Diversification (BAL) • Diversifying Towards the Topic Sentiment (CRD) • Diversifying Against the Topic Sentiment (OTL) • sorted in increasing order of P(σ|T) according to CRD • reverse

  12. Diversity by Proportionality

  13. PM-2M lσ, the actual number of documents with senti- mentσ in the retrieved top K set. For a large enough rank K, this may result in a suboptimally diversified list where documents with an over-emphasized sentiment are exploited early in the ranks.

  14. Outline • Introduction • Approach • Experiment • Conclusion

  15. Evaluation Measure

  16. Straight-Bias Experiments

  17. Straight-Bias Experiments

  18. Straight-Bias Experiments

  19. Cross-Bias Experiments Consider

  20. Cross-Bias Experiments Consider

  21. Outline • Introduction • Approach • Experiment • Conclusion

  22. Conclusion • In this paper we demonstrate how to diversify search results according to sentiments by considering a pre-defined bias. This allows us to emphasize either majority or minority sentiments during diversification, or to give an unbiased representation across all sentiment aspects.

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