How to Extract Insights from Chat Logs and Conversations Using Semantics

Chat logs and conversation data contain valuable insights, but extracting meaningful patterns from unstructured dialogue requires more than simple keyword searches. Traditional text processing falls short when dealing with context, intent, and semantic relationships. This guide shows how to use semantic operations on conversation data to extract actionable insights at scale.

The Challenge with Conventional Chat Log Analysis

Most organizations collect conversation data from support chats, sales calls, team communications, or user feedback. The standard approach involves keyword searches, regex patterns, or custom scripts. These methods break down quickly:

Rigid pattern matching fails to capture meaning. A support chat mentioning "can't log in" and another saying "authentication isn't working" describe the same issue, but string matching treats them as different problems.

Context gets lost in aggregation. Sentiment analysis on individual messages misses the conversation arc. A frustrated opening can resolve into satisfaction, but isolated message scoring misses that progression.

Manual categorization doesn't scale. Human reviewers can classify hundreds of conversations, not thousands. As volume grows, coverage drops and backlogs pile up.

Multi-turn conversations require stateful understanding. Unlike isolated documents, chat logs have temporal structure. Who said what, when, and in response to what question all matter for proper analysis.

Semantic processing addresses these limitations by understanding meaning, not just text patterns. Fenic, Typedef's DataFrame framework for AI applications, brings semantic operations directly into data pipelines.

Why DataFrame Operations for Conversation Analysis

Chat logs are data. They arrive in various formats (JSON exports, CSV files, transcript formats), need parsing and transformation, and produce structured insights. DataFrames provide the natural abstraction for these operations.

Fenic extends the familiar DataFrame API with semantic operators. Instead of writing custom LLM integration code, you call semantic.classify(), semantic.extract(), or semantic.join() as standard column operations. The framework handles model orchestration, rate limits, batching, and error handling.

This approach keeps conversations queryable and transformations auditable. Every semantic operation is a DataFrame transformation with inputs, outputs, and lineage tracking.

Setting Up Fenic for Semantic Analysis

Install Fenic and configure a session with your model providers:

python
from fenic.api.session import Session
from fenic.api.session.config import SessionConfig, SemanticConfig, OpenAILanguageModel

config = SessionConfig(
    app_name="conversation_analysis",
    semantic=SemanticConfig(
        language_models={
            "gpt4": OpenAILanguageModel(
                model_name="gpt-4o-mini",
                rpm=100,
                tpm=100000
            )
        }
    )
)

session = Session.get_or_create(config)

This configuration sets rate limits and model selection. Fenic automatically batches requests and handles throttling, so your pipeline stays reliable at scale.

Parsing Conversation Formats into Structured Data

Chat logs arrive in multiple formats. Fenic's parse_transcript() function normalizes three common formats into a unified schema.

Supported Formats

SRT (SubRip Subtitle)

1
00:00:01,000 --> 00:00:05,000
Customer: I can't access my account

2
00:00:05,500 --> 00:00:10,000
Agent: Let me look that up for you

WebVTT (Web Video Text Tracks)

WEBVTT

Customer
00:00:01.000 --> 00:00:05.000
I can't access my account

Agent
00:00:05.500 --> 00:00:10.000
Let me look that up for you

Generic Conversation

[00:00:01] Customer: I can't access my account
[00:00:05] Agent: Let me look that up for you

Parsing Implementation

python
import fenic.api.functions as fc
from fenic.api.functions import text

# Load conversation files
df = session.read.docs(
    "conversations/**/*.txt",
    content_type="markdown",
    recursive=True
)

# Parse transcript format
parsed_df = df.select(
    fc.col("file_path"),
    text.parse_transcript(fc.col("content"), "srt").alias("segments")
)

# Explode into individual messages
messages_df = parsed_df.select(
    fc.col("file_path"),
    fc.explode(fc.col("segments")).alias("segment")
).select(
    fc.col("file_path"),
    fc.col("segment.speaker").alias("speaker"),
    fc.col("segment.start_time").alias("timestamp"),
    fc.col("segment.content").alias("text")
)

The unified schema provides:

• speaker: Who said it
• timestamp: When (in seconds)
• text: What was said
• format: Original format type

This structure lets you apply semantic operations consistently across different source formats.

Classifying Conversation Intent and Topics

Classification tags conversations with predefined categories. This works better than keyword matching because it understands semantic similarity.

python
from fenic.api.functions import semantic

# Classify support conversations by issue type
classified_df = messages_df.with_column(
    "issue_category",
    semantic.classify(
        fc.col("text"),
        ["Account Access", "Payment Issue", "Technical Problem", "Product Question"]
    )
)

For better accuracy, use ClassDefinition objects with descriptions:

python
from fenic.core.types.classify import ClassDefinition

categories = [
    ClassDefinition(
        label="Account Access",
        description="Login problems, password resets, account recovery"
    ),
    ClassDefinition(
        label="Payment Issue",
        description="Billing questions, failed transactions, refund requests"
    ),
    ClassDefinition(
        label="Technical Problem",
        description="Application bugs, feature malfunctions, system errors"
    )
]

classified_df = messages_df.with_column(
    "issue_category",
    semantic.classify(fc.col("text"), categories)
)

Classification works per message or per conversation. Aggregate classifications to understand conversation-level topics:

python
# Get primary topic per conversation
conversation_topics = classified_df.group_by("file_path").agg(
    fc.mode(fc.col("issue_category")).alias("primary_topic"),
    fc.count("*").alias("message_count")
)

Learn more about building reliable AI pipelines with semantic operators.

Extracting Structured Information from Dialogue

Conversations contain structured information hidden in unstructured text. Extraction pulls specific fields into typed schemas using Pydantic models.

python
from pydantic import BaseModel, Field
from typing import List

class ActionItem(BaseModel):
    task: str = Field(description="What needs to be done")
    assignee: str = Field(description="Who is responsible")
    deadline: str = Field(description="Due date if mentioned")

# Extract action items from meeting transcripts
actions_df = messages_df.select(
    fc.col("file_path"),
    fc.col("text"),
    semantic.extract(fc.col("text"), ActionItem).alias("action")
).filter(
    fc.col("action").is_not_null()
)

Extract multiple entities at once with nested models:

python
class CustomerInfo(BaseModel):
    name: str = Field(description="Customer's name")
    account_id: str = Field(description="Account identifier")
    issue_description: str = Field(description="Main problem described")
    sentiment: str = Field(description="Customer's emotional state")

# Extract structured data from support conversations
info_df = messages_df.with_column(
    "customer_info",
    semantic.extract(fc.col("text"), CustomerInfo)
).with_column(
    "name", fc.col("customer_info.name")
).with_column(
    "account", fc.col("customer_info.account_id")
).with_column(
    "issue", fc.col("customer_info.issue_description")
)

Extraction handles optional fields naturally. Missing information produces null values without breaking the pipeline.

Analyzing Sentiment Across Conversations

Sentiment analysis on chat logs reveals emotional progression through conversations. Fenic's analyze_sentiment() function returns positive, negative, or neutral classifications.

python
# Add sentiment to each message
sentiment_df = messages_df.with_column(
    "sentiment",
    semantic.analyze_sentiment(fc.col("text"))
)

# Track sentiment changes over conversation
sentiment_flow = sentiment_df.select(
    fc.col("file_path"),
    fc.col("speaker"),
    fc.col("timestamp"),
    fc.col("sentiment")
).order_by("file_path", "timestamp")

Aggregate sentiment by speaker to find patterns:

python
# Speaker-level sentiment distribution
speaker_sentiment = sentiment_df.group_by("speaker").agg(
    fc.count("*").alias("total_messages"),
    fc.sum(
        fc.when(fc.col("sentiment") == "positive", 1).otherwise(0)
    ).alias("positive_count"),
    fc.sum(
        fc.when(fc.col("sentiment") == "negative", 1).otherwise(0)
    ).alias("negative_count")
)

Detect sentiment shifts within conversations:

python
# Calculate sentiment score trend
sentiment_score = sentiment_df.with_column(
    "score",
    fc.when(fc.col("sentiment") == "positive", 1)
    .when(fc.col("sentiment") == "negative", -1)
    .otherwise(0)
)

# Track cumulative sentiment
from fenic.api.window import Window

window_spec = Window.partition_by("file_path").order_by("timestamp")

trend_df = sentiment_score.with_column(
    "cumulative_sentiment",
    fc.sum(fc.col("score")).over(window_spec)
)

This approach reveals whether conversations improve or deteriorate emotionally, which matters for support quality and customer satisfaction analysis.

Filtering Conversations with Semantic Predicates

Semantic predicates enable natural language filtering beyond exact string matches. Use semantic.predicate() to find messages matching complex conditions.

python
# Find messages indicating customer frustration
frustrated = messages_df.filter(
    semantic.predicate(
        "This message expresses frustration, anger, or dissatisfaction: {{ text }}",
        text=fc.col("text")
    )
)

Combine semantic and traditional filters:

python
# Find recent urgent support issues
urgent_recent = messages_df.filter(
    (fc.col("timestamp") > fc.lit(86400)) &  # Last 24 hours
    semantic.predicate(
        "This support message indicates an urgent or critical issue: {{ text }}",
        text=fc.col("text")
    )
)

Use predicates for multi-column reasoning:

python
# Find conversations where agent response matched customer question
relevant_responses = messages_df.filter(
    semantic.predicate(
        """
        Customer question: {{ question }}
        Agent response: {{ response }}
        The agent directly addressed the customer's question.
        """,
        question=fc.col("previous_message"),
        response=fc.col("text")
    )
)

Semantic predicates handle variations in phrasing that keyword matching misses. "I'm furious," "this is unacceptable," and "terrible experience" all match a frustration predicate despite different wording.

Generating Summaries and Insights with Semantic Reduction

Semantic reduction aggregates multiple messages into concise summaries. The semantic.reduce() function applies natural language instructions to grouped text.

python
# Summarize each conversation
conversation_summaries = messages_df.group_by("file_path").agg(
    semantic.reduce(
        "Summarize this conversation highlighting the main issue and resolution",
        fc.col("text"),
        order_by=fc.col("timestamp")
    ).alias("summary")
)

The order_by parameter preserves temporal sequence, ensuring summaries respect conversation flow.

Generate different types of insights:

python
# Key decisions per conversation
decisions = messages_df.group_by("file_path").agg(
    semantic.reduce(
        "List the key decisions made in this conversation",
        fc.col("text"),
        order_by=fc.col("timestamp")
    ).alias("decisions")
)

# Action items per meeting
actions = messages_df.group_by("file_path").agg(
    semantic.reduce(
        "Extract action items with owners and deadlines",
        fc.col("text"),
        order_by=fc.col("timestamp")
    ).alias("action_items")
)

Use group context to customize summaries:

python
# Summaries with conversation metadata
enriched_summaries = messages_df.group_by("file_path", "primary_speaker").agg(
    semantic.reduce(
        "Summarize this {{ primary_speaker }} conversation focusing on customer outcomes",
        fc.col("text"),
        group_context={"primary_speaker": fc.col("primary_speaker")},
        order_by=fc.col("timestamp")
    ).alias("summary")
)

Semantic reduction works for any text aggregation where you need coherent synthesis rather than concatenation.

Joining Conversations with Related Data

Semantic joins connect conversations based on meaning rather than exact matches. This enables matching support chats with knowledge base articles, or linking conversations discussing similar issues.

python
# Load knowledge base articles
kb_df = session.read.docs("knowledge_base/*.md", content_type="markdown")

# Join conversations with relevant KB articles
from textwrap import dedent

matched = messages_df.semantic.join(
    other=kb_df,
    predicate=dedent('''
        Customer Message: {{ left_on }}
        Knowledge Base Article: {{ right_on }}
        This article would help resolve the customer's issue.
    '''),
    left_on=fc.col("text"),
    right_on=fc.col("content")
)

Find related conversations across different time periods:

python
# Load historical conversations
recent = messages_df.filter(fc.col("timestamp") > fc.lit(604800))  # Last week
historical = messages_df.filter(fc.col("timestamp") <= fc.lit(604800))

# Find similar issues discussed before
similar_issues = recent.semantic.join(
    other=historical,
    predicate="""
        Recent conversation: {{ left_on }}
        Past conversation: {{ right_on }}
        These conversations discuss the same underlying problem.
    """,
    left_on=fc.col("text"),
    right_on=fc.col("text")
)

Semantic joins power sophisticated analysis like:

• Matching customer questions to FAQ answers
• Linking conversations about related products
• Finding duplicate support tickets
• Connecting sales conversations to existing case studies

The join returns combined DataFrames with columns from both sides, enabling downstream analysis on matched pairs.

Clustering Conversations by Semantic Similarity

Clustering groups conversations by semantic similarity rather than keyword overlap. This reveals thematic patterns across large conversation sets.

python
# Generate embeddings for conversation text
embedded = messages_df.with_column(
    "embedding",
    semantic.embed(fc.col("text"))
)

# Cluster using K-means
clustered = embedded.semantic.with_cluster_labels(
    by="embedding",
    num_clusters=10
)

Analyze cluster characteristics:

python
# Get most representative messages per cluster
cluster_exemplars = clustered.group_by("cluster_label").agg(
    fc.count("*").alias("conversation_count"),
    fc.first(fc.col("text")).alias("example_message")
)

# Examine cluster topics
cluster_topics = clustered.group_by("cluster_label").agg(
    semantic.reduce(
        "Describe the common theme across these conversations",
        fc.col("text")
    ).alias("cluster_theme")
)

Use clusters to route conversations or prioritize review:

python
# Find high-urgency clusters
urgent_clusters = clustered.filter(
    semantic.predicate(
        "These conversations indicate urgent customer issues: {{ text }}",
        text=fc.col("text")
    )
).group_by("cluster_label").agg(
    fc.count("*").alias("urgent_count")
)

Clustering scales to thousands of conversations and reveals patterns humans would miss in manual review. See the log clustering and triage guide for a detailed implementation.

Temporal Analysis of Conversation Patterns

Chat logs have time-based structure. Analyzing temporal patterns reveals how conversations evolve and when issues arise.

python
# Add time-based windows
from fenic.api.functions import col

windowed = messages_df.with_column(
    "hour_bucket",
    (fc.col("timestamp") / 3600).cast("integer")
).with_column(
    "day_bucket",
    (fc.col("timestamp") / 86400).cast("integer")
)

# Hourly conversation volume
hourly_volume = windowed.group_by("hour_bucket").agg(
    fc.count("*").alias("message_count"),
    fc.count_distinct("file_path").alias("conversation_count")
)

Track topic trends over time:

python
# Classify messages, then aggregate by time
classified_temporal = messages_df.with_column(
    "category",
    semantic.classify(fc.col("text"), ["Bug Report", "Feature Request", "Question"])
).with_column(
    "week",
    (fc.col("timestamp") / 604800).cast("integer")
)

# Weekly category distribution
weekly_trends = classified_temporal.group_by("week", "category").agg(
    fc.count("*").alias("count")
)

Identify sentiment changes throughout conversations:

python
# Conversation-level sentiment progression
sentiment_progression = messages_df.with_column(
    "sentiment",
    semantic.analyze_sentiment(fc.col("text"))
).with_column(
    "message_index",
    fc.row_number().over(
        Window.partition_by("file_path").order_by("timestamp")
    )
)

# First message vs last message sentiment
sentiment_change = sentiment_progression.group_by("file_path").agg(
    fc.first(fc.col("sentiment")).alias("initial_sentiment"),
    fc.last(fc.col("sentiment")).alias("final_sentiment")
)

Temporal analysis answers questions like: When do support volumes spike? Which issues trend upward? Do conversations end more positively than they start?

Creating Queryable Tools with Model Context Protocol

Make conversation insights accessible to agents and applications using the Model Context Protocol (MCP). MCP exposes DataFrame queries as callable tools.

python
from fenic.core.mcp.types import ToolParam
from fenic.core.types import StringType, IntegerType
from fenic.api.functions import text

# Save processed conversations
messages_df.write.save_as_table("conversations", mode="overwrite")

# Create search tool
session.catalog.create_tool(
    tool_name="search_conversations",
    tool_description="Search conversation messages by keyword or topic",
    tool_query=session.table("conversations").filter(
        fc.col("text").like(
            text.concat(fc.lit("%"), fc.tool_param("search_term", StringType), fc.lit("%"))
        )
    ).limit(fc.tool_param("limit", IntegerType)),
    tool_params=[
        ToolParam(
            name="search_term",
            description="Keyword or phrase to find",
            has_default=True,
            default_value=""
        ),
        ToolParam(
            name="limit",
            description="Maximum results to return",
            has_default=True,
            default_value=20
        )
    ]
)

Create analytics tools:

python
# Conversation statistics by category
session.catalog.create_tool(
    tool_name="category_stats",
    tool_description="Get conversation counts and sentiment by category",
    tool_query=session.table("conversations").filter(
        fc.col("issue_category") == fc.tool_param("category", StringType)
    ).agg(
        fc.count("*").alias("total_messages"),
        fc.count_distinct("file_path").alias("conversation_count"),
        fc.avg(
            fc.when(fc.col("sentiment") == "positive", 1)
            .when(fc.col("sentiment") == "negative", -1)
            .otherwise(0)
        ).alias("avg_sentiment_score")
    ),
    tool_params=[
        ToolParam(
            name="category",
            description="Issue category to analyze",
            has_default=True,
            default_value="Technical Problem"
        )
    ]
)

Launch an MCP server:

python
from fenic.api.mcp import create_mcp_server, run_mcp_server_sync

# Get registered tools
tools = session.catalog.list_tools()

# Create and run server
server = create_mcp_server(
    session,
    "ConversationAnalysisServer",
    user_defined_tools=tools,
    concurrency_limit=8
)

run_mcp_server_sync(
    server,
    transport="http",
    stateless_http=True,
    port=8000,
    host="127.0.0.1",
    path="/mcp"
)

Agents can now query conversation data through well-defined tools without direct DataFrame access. See the transcript processing guide for integration with LangChain.

Production Deployment Patterns

Production conversation analysis requires reliability, monitoring, and efficient resource usage.

Batch Processing for Scale

Process conversations in batches to maximize throughput:

python
from fenic.api.session.config import SessionConfig, SemanticConfig, OpenAILanguageModel

# Configure model for higher throughput
config = SessionConfig(
    app_name="conversation_batch",
    semantic=SemanticConfig(
        language_models={
            "fast": OpenAILanguageModel(
                model_name="gpt-4o-mini",
                rpm=500,  # Higher rate limit
                tpm=200000
            )
        }
    )
)

# Process in manageable chunks
for batch in conversation_batches:
    batch_df = session.create_dataframe(batch)

    # Apply transformations
    processed = (batch_df
        .with_column("category", semantic.classify(fc.col("text"), categories))
        .with_column("sentiment", semantic.analyze_sentiment(fc.col("text")))
    )

    # Persist results
    processed.write.save_as_table(
        "processed_conversations",
        mode="append"
    )

Cost Management

Control inference costs through strategic processing:

python
# Deduplicate before expensive operations
unique_messages = messages_df.select(
    fc.col("text")
).distinct()

# Process unique messages only
processed_unique = unique_messages.with_column(
    "classification",
    semantic.classify(fc.col("text"), categories)
)

# Join back to original data
full_df = messages_df.join(
    processed_unique,
    on="text",
    how="left"
)

Error Handling

Handle failures gracefully in production:

python
# Add error recovery columns
safe_df = messages_df.with_column(
    "classification",
    fc.when(
        fc.col("text").is_not_null() & (fc.length(fc.col("text")) > 0),
        semantic.classify(fc.col("text"), categories)
    ).otherwise(fc.lit("unclassified"))
)

# Log failed records
failed = messages_df.filter(
    fc.col("text").is_null() | (fc.length(fc.col("text")) == 0)
).select(
    fc.col("file_path"),
    fc.lit("Empty or null message").alias("error_reason")
)

failed.write.csv("processing_errors.csv")

Monitoring and Metrics

Track pipeline performance:

python
# Access built-in metrics
metrics = session.table("fenic_system.query_metrics")

# Aggregate by operation type
operation_stats = metrics.group_by("operation").agg(
    fc.count("*").alias("call_count"),
    fc.sum("cost_usd").alias("total_cost"),
    fc.avg("latency_ms").alias("avg_latency_ms"),
    fc.sum("input_tokens").alias("total_input_tokens")
)

operation_stats.show()

Production systems benefit from caching intermediate results, monitoring token usage, and implementing circuit breakers for model failures.

Complete Conversation Analysis Pipeline

Here's a full implementation combining the techniques above:

python
from fenic.api.session import Session
from fenic.api.session.config import SessionConfig, SemanticConfig, OpenAILanguageModel
import fenic.api.functions as fc
from fenic.api.functions import text, semantic
from pydantic import BaseModel, Field

# Setup
config = SessionConfig(
    app_name="conversation_insights",
    semantic=SemanticConfig(
        language_models={
            "gpt4": OpenAILanguageModel(
                model_name="gpt-4o-mini",
                rpm=100,
                tpm=100000
            )
        }
    )
)
session = Session.get_or_create(config)

# Load and parse conversations
raw_df = session.read.docs(
    "conversations/**/*.srt",
    content_type="markdown",
    recursive=True
)

parsed_df = raw_df.select(
    fc.col("file_path"),
    text.parse_transcript(fc.col("content"), "srt").alias("segments")
)

messages_df = parsed_df.select(
    fc.col("file_path"),
    fc.explode(fc.col("segments")).alias("seg")
).select(
    fc.col("file_path"),
    fc.col("seg.speaker").alias("speaker"),
    fc.col("seg.start_time").alias("timestamp"),
    fc.col("seg.content").alias("text")
)

# Enrich with semantic analysis
enriched_df = (messages_df
    .with_column(
        "sentiment",
        semantic.analyze_sentiment(fc.col("text"))
    )
    .with_column(
        "category",
        semantic.classify(
            fc.col("text"),
            ["Technical Issue", "Billing Question", "Feature Request", "General"]
        )
    )
)

# Generate conversation-level summaries
summaries_df = enriched_df.group_by("file_path").agg(
    fc.count("*").alias("message_count"),
    fc.count_distinct("speaker").alias("speaker_count"),
    semantic.reduce(
        "Summarize this conversation focusing on key issues and outcomes",
        fc.col("text"),
        order_by=fc.col("timestamp")
    ).alias("summary")
)

# Save results
enriched_df.write.save_as_table("enriched_conversations", mode="overwrite")
summaries_df.write.csv("conversation_summaries.csv")

# Create MCP tools
from fenic.core.mcp.types import ToolParam
from fenic.core.types import StringType

session.catalog.create_tool(
    tool_name="search_by_category",
    tool_description="Find conversations by issue category",
    tool_query=session.table("enriched_conversations").filter(
        fc.col("category") == fc.tool_param("category", StringType)
    ),
    tool_params=[
        ToolParam(
            name="category",
            description="Issue category to filter by",
            has_default=True,
            default_value="Technical Issue"
        )
    ]
)

print("Pipeline complete. Results saved and tools registered.")

This pipeline processes conversations from raw text through semantic enrichment to queryable insights.

Real-World Applications

Organizations use semantic conversation analysis for multiple use cases:

Support Operations: Automatically categorize incoming support chats, route to appropriate teams, and identify escalation-worthy conversations. Track resolution patterns and agent performance through sentiment progression analysis. RudderStack reduced triage time by 95% using similar semantic classification and context extraction.

Sales Intelligence: Extract buying signals from sales call transcripts, identify objections and concerns, and match conversations to relevant case studies or product information. Semantic joins connect prospect questions with existing solutions.

Product Feedback: Cluster feature requests to find common themes, extract specific improvement suggestions, and track sentiment around product areas over time. Classification separates bugs from feature requests from general questions.

Meeting Analytics: Generate action items from meeting transcripts, identify decisions and owners, and create searchable knowledge bases from conversation history. Reduction operations turn hour-long meetings into structured outputs.

Community Moderation: Detect problematic content beyond keyword lists, identify harassment or abuse through semantic understanding, and flag conversations requiring human review based on context.

The common thread: semantic operations handle variations in language that rule-based systems miss, scale to thousands of conversations, and produce structured outputs for downstream systems.

Getting Started

Start with a small set of conversations and basic semantic operations:

1. Load 10-20 representative conversations
2. Parse into structured format with parse_transcript()
3. Apply one semantic operation (classification or sentiment)
4. Examine results and adjust categories or prompts
5. Expand to full dataset and add more operations

Fenic's local-first design means you can prototype entirely on your machine before scaling to production. The same code runs locally during development and in cloud deployment.

For more examples and ready-to-run implementations, explore the Fenic documentation, GitHub repository, and example notebooks.

---

Semantic processing transforms conversation data from text dumps into actionable insights. By treating semantic operations as DataFrame primitives, Fenic makes sophisticated analysis accessible without custom LLM integration code. The result is scalable, auditable, and production-ready conversation analysis that actually understands what people are saying.