Conversational Dataset
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A Conversational Dataset is session dataset that contains conversational records (which capture the content, context, and structure of conversations between two or more participants)
- Context:
- It can (typically) include text exchanges via messaging apps, voice-based dialogues from phone calls or voice assistant interactions, and multimodal communications that combine text, voice, images, and videos.
- It can (often) be analyzed to extract insights related to customer preferences, sentiment analysis, conversational patterns, and intent recognition.
- It can be used to train Natural Language Processing (NLP) models, particularly in the development of chatbots and virtual assistants.
- It can include metadata such as timestamps, participant identifiers, and conversation status, which provide additional context for analysis.
- It can be subject to privacy and ethical considerations, especially when it contains personally identifiable information or sensitive content.
- It can be sourced from public domains or collected through proprietary means, with considerations for licensing and ethical use.
- It can include annotated data for specific tasks such as sentiment analysis, intent recognition, and dialogue act classification, facilitating supervised learning in machine learning models.
- It can vary greatly in size, from hundreds of conversational instances to billions, affecting the model's performance and generalizability.
- It can (often) require preprocessing steps such as tokenization, anonymization, and normalization to be effectively used in NLP tasks.
- ...
- Example(s):
- A Chatbot Interaction Data.
- The Reddit Comments Corpus from Defined AI, which includes over 1.7 billion comments from the Reddit platform, providing a vast resource of colloquial language and diverse topics``【oaicite:3】``.
- The Cornell Movie-Dialogs Corpus available through ConvoKit, consisting of fictional conversations extracted from movie scripts, offering a rich dataset for studying narrative dialogues and character interactions``【oaicite:2】``.
- The Twitter US Airline Sentiment Corpus on Kaggle, featuring customer service interactions in the form of tweets to US airlines, tagged with sentiment labels, useful for sentiment analysis tasks``【oaicite:1】``.
- The Enron Email Corpus, comprising over 600,000 emails from the Enron Corporation, which is frequently used for research in communication patterns and email classification tasks``【oaicite:0】``.
- A transcript of a customer service chat session, which includes the customer's queries and the service representative's responses.
- A recording of a voice command given to a smart home device, along with the device's verbal response.
- A collection of text messages exchanged between users on a social media platform discussing a specific topic.
- ...
- Counter-Example(s):
- [[[Non-interactive data]], such as a news article or a static report.
- Structured data in databases that do not contain conversational elements, such as financial records or inventory lists.
- See: Natural Language Processing, Chatbot, Virtual Assistant, Sentiment Analysis, Intent Recognition.