Unlocking Customer Insights: An overview to Analyzing Support Conversations using Natural Language Processing

Summarizing and synthesizing trends from a large number of conversation threads can be achieved through a variety of techniques. The best approach usually depends on the nature of your data and the resources available to you. Here are a few methods you could use:

  1. Topic Modeling: This is a type of statistical model used to uncover the abstract "topics" that occur in a collection of documents. Latent Dirichlet Allocation (LDA) is a common method for doing this. LDA can analyze the words in each document (in this case, a conversation thread) and group them into topics. For example, words like "invoice", "payment", and "credit card" might be grouped into a "Billing" topic.
  2. Text Classification: If you have labeled data (or the resources to label your data), you could train a supervised machine learning model to classify your conversation threads into predefined categories. This could give you a high-level understanding of what categories of topics are most common in your data.
  3. Keyword Extraction: This method involves identifying the most commonly occurring words or phrases in your data. This could be a simpler and more resource-efficient method than the others, though it might not capture the nuances of your data as well.
  4. Sentiment Analysis: This could be useful if you're interested in how users feel about certain topics. Sentiment analysis involves determining whether a piece of text is positive, negative, or neutral. You could use this to identify trends in user sentiment over time or in relation to certain topics.
  5. Question/Answer Understanding: For more complex understanding, models like GPT-3 can be used to understand the questions being asked in the conversation threads. By extracting the main questions, you can understand the key concerns or inquiries users have.
  6. Named Entity Recognition (NER): This involves identifying and categorizing named entities (like person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.) in your text. This could be useful if you want to understand what specific products, services, or other entities users are talking about.

In practice, you might use a combination of these methods to analyze your data. For example, you could use topic modeling to identify general trends, and then use text classification or keyword extraction to dig deeper into specific topics of interest. Similarly, sentiment analysis or NER could be used to provide additional context for your findings. After using these techniques, you'd typically visualize the results in a way that clearly communicates the major trends and topics.