NLP conversation analysis techniques
research compiled from academic sources and industry practices.
1. sentiment analysis
core approach
- score: ranges -1.0 (negative) to +1.0 (positive), indicates emotional leaning
- magnitude: 0.0 to +inf, indicates strength of emotion regardless of polarity
- mixed sentiment: high magnitude with neutral score signals conflicting emotions within text
interpretation nuances
- neutral score + high magnitude = mixed emotions (not truly neutral)
- neutral score + low magnitude = genuinely neutral content
- per-sentence analysis needed for multi-turn conversations to avoid averaging artifacts
tools
- google natural language API
- VADER (parsimonious rule-based model for social media text) - cited by Hutto & Gilbert 2014
- machine learning approaches outperform dictionary methods for disclosure sentiment (Frankel et al., 2022)
2. topic modeling
challenges in dialogue
- traditional LDA poorly suited for conversations because:
- turns are too short for reliable word co-occurrence
- many turns contain no topic-relevant info (“why is that?” works in any topic)
- topic models remove pronouns but pronouns carry meaning in dialogue
- topic segmentation harder than topic assignment (Purver, 2011)
recommended approaches
- domain-specific rules: for scripted interactions (sales calls, customer service), use known dialogue scripts to segment into stages
- preassigned topic lists: makes ex-post segmentation easier
- contextual topic modeling: incorporate conversational context and dialog act features for 35% relative accuracy gains (Khatri et al., 2018)
- topical depth correlates with coherence and engagement metrics
tools
- LDA for rough exploration only
- ConvoKit (Python) - toolkit for conversation analysis (Chang et al., 2020)
3. conversation flow analysis
turn-taking patterns
- turn-taking: fundamental aspect - who speaks when, how transitions happen
- floor holding: speaker continues despite interruption attempts
- overlapping talk: speakers talk simultaneously, signals communication breakdown
- adjacency pairs: question-answer, greeting-response, invitation-acceptance pairs
structural features
- timing features: incorporate timestamps from transcripts
- interactive features: look at consecutive turn sequences
- repair sequences: how participants fix communication breakdowns
metrics
- average conversation length (messages per conversation)
- interaction frequency (daily/weekly/monthly patterns)
- response time between turns
4. user behavior patterns
engagement patterns
- progressive disclosure: reveal info gradually based on user needs
- satisficing: users prefer accessible satisfactory options over optimal ones
- instant gratification: users engage more with products that reward quickly
- deferred choices: analysis paralysis when asked for too much upfront
analysis techniques
- funnel analysis: track progression through conversion stages, identify drop-off points
- path analysis: track all paths users take to complete actions, find “happy path”
- cohort analysis: track engagement/retention over time for user segments
- trend analysis: identify seasonal/temporal behavior shifts
5. linguistic feature extraction
static text features
- word counts, n-grams
- dictionary-based categorization (LIWC-style)
- sentence structure parsing (subjects, verbs, objects)
- named entity recognition
dialogue-specific features
- speaker-level aggregation: collapse turns by speaker for analysis
- turn-level analysis: examine individual turns in sequence
- interactivity markers: responsiveness, question types, acknowledgments
key insight
single-voice document analysis tools require adaptation for dialogue - must handle:
- highly variable turn lengths
- speaker identity tracking
- temporal ordering
6. practical tools
| tool | language | purpose |
|---|---|---|
| ConvoKit | Python | full conversation analysis toolkit |
| VADER | Python | social media sentiment |
| spaCy | Python | NLP parsing, NER |
| tidytext | R | text mining |
| quanteda | R | quantitative text analysis |
7. best practices for chat log analysis
- structure data properly: maintain both turn-level and speaker-level datasets, link them
- account for turn variability: short turns may lack signal, aggregate thoughtfully
- preserve temporal info: timestamps enable timing-based features
- validate with humans: machine-extracted features should correlate with human judgment
- benchmark against baselines: compare complex models to simple word-count/sentiment baselines
sources
- Yeomans et al. (2023) “A Practical Guide to Conversation Research” - SAGE
- Google Cloud Agent Assist sentiment documentation
- Khatri et al. (2018) “Contextual Topic Modeling For Dialog Systems” - arXiv
- Skantze (2021) “Turn-taking in Conversational Systems” - Computer Speech & Language
- Hutto & Gilbert (2014) VADER sentiment analysis
- Chang et al. (2020) ConvoKit - SIGDIAL