Machine learning techniques for persuasion detection in conversation

Citation: Pedro Ortiz (2010) Machine learning techniques for persuasion detection in conversation. Naval Postgraduate School (RSS)
Internet Archive Scholar (search for fulltext): Machine learning techniques for persuasion detection in conversation
Tagged: Computer Science (RSS) persuasion (RSS), argumentation mining (RSS), machine learning (RSS), theses (RSS)

Summary

The core question of this Master's thesis, as the author puts it, is: “Can we learn to identify persuasion as characterized by Cialdini’s model using traditional machine learning techniques?” The authors give a qualified "yes"; improvement is needed for real-world results, but the methods function. The corpus used was developed in his colleague's Master's thesis, Persuasion detection in conversation.

Persuasion model

The persuasion model is taken from Cialdini; see Cialdini's 6 key principles of persuasion; examples are provided on pages 5-8.

Features

• Word unigrams and bigrams
• gappy word bigrams
• orthogonal sparse word bigrams
• feature discrimination (stop words, entropy-base pruning)
• texttiling -- segmentation using three stages (see Hearst [14] and Nomoto and Nitta [15])

1. tokenization
2. lexical score determination ("based on block comparison and vocabulary introduction")
3. boundary identification

Machine Learning Techniques

Naive Bayes with add-one smoothing, Maximum Entropy, and Vector_Machine SVMs were used. Results were evaluated with precision, recall, and F-score.

Evaluation

• Precision
• Recall
• Accuracy - "the number of corect classifications in proportion to the size of the set being classified"

Selected references

Persuasion model

• R. Cialdini, Influence: The psychology of persuasion. New York, NY: Collins, 2007.

Texttiling

[14] M. Hearst, “Texttiling: Segmenting text into multi-paragraph subtopic passages,” Computational Linguistics, vol. 23, no. 1, pp. 33–64, 1997. [15] T. Nomoto and Y. Nitta, “A grammatico-statistical approach to discourse partitioning,” in Proceedings of the 15th Conference on Computational Linguistics. Morristown, NJ: Association for Computational Linguistics, 1994, pp. 1145–1150.

Previous work in Persuasion detection

• W.-H. Lin, T. Wilson, J. Wiebe, and A. Hauptmann, “Which side are you on?: Identifying perspectives at the document and sentence levels,” in CoNLL-X ’06: Proceedings of the Tenth Conference on Computational Natural Language Learning. Morristown, NJ, USA: Association for Computational Linguistics, 2006, pp. 109–116.

• D. Bikel and J. Sorensen, “If we want your opinion,” in ICSC ’07: Proceedings of the International Conference on Semantic Computing. Washington D.C.: IEEE Computer So- ciety, 2007, pp. 493–500.

• H. T. Gilbert, “Persuasion detection in conversation,” Master’s thesis, Naval Postgraduate School, Monterey, CA, 2010.

Interesting Observations

"the transcripts from the Davidian standoff in Waco, Texas were significantly different from the rest of the corpus." -- May have bearing for other sciences studying these.

Useful summaries

Summaries of machine learning techniques given are particularly interesting.

Suggestions for future work

The author identifies several areas of future work needed:

Data set improvements

• more and larger data sets
• additional genres such as Web pages, blogs, and SMS messages
• additional information
  • belief annotations
  • distance from the previous persuasive post
  • correct speaker tags
  • dialogue act tags

Features set improvements

• topic models
• combining high recall features with high precision features

Future research

• segmentation schemes
• effects of time and sequence
• the utility of bagging, boosting, and voting
• the role of speaker type
• the impact of parts of speech and syntax

See also

• Persuasion detection in conversation, his colleague's master's thesis, which created a corpus
• A microtext corpus for persuasion detection in dialog, work by these two students and others