Notes on Text Analytics and Sentiment Analysis

Chatbots Overview

  • Definition: A chatbot is a computer program that simulates conversation with users over the internet using artificial intelligence (AI) and natural language processing (NLP) technologies.

  • Historical Context: The original ELIZA program, reated in the 1960s, emulated a Rogerian therapist, successfully convincing some users it was human-like by engaging in self-referential conversation.

  • Capabilities: Chatbots can perform various tasks, such as:

    • Providing information

    • Answering queries

    • Offering recommendations

    • Facilitating transactions

    • Executing simple tasks

Text Analytics Overview

  • Definition: Text analytics (or text mining) is a semi-automated process for extracting patterns and insights from unstructured textual data.

  • Context: About 85% of organizational data is unstructured, increasing by 18% every year, making text analytics critical for decision-making and gaining a competitive edge.

Text Analytics Process

  1. Establish the Corpus: Collect and organize domain-specific documents (e.g., PDFs, emails, webpages).

  2. Preprocess the Data: Introduce structure to the corpus.

  3. Extract Knowledge: Discover patterns from the preprocessed data.

Important Terminology

  • Corpus: Collection of documents for processing.

  • Terms: Single words or phrases extracted from the corpus (e.g., "cat", "dog").

  • Concepts: Higher-level abstractions that group related terms.

  • Tokenizing: Splitting text into smaller units or tokens (e.g., splitting "the fish swam away" into "the", "fish", "swam", "away").

  • Stop Words: Common words filtered out during processing as they provide little value (e.g., "and", "the").

  • Stemming: Reducing words to their base or root forms (e.g., "running" to "run").

  • Lemmatization: Similar to stemming but considers context and part-of-speech (e.g., "better" to "good").

  • Part-of-Speech Tagging: Marking words in a text as nouns, verbs, etc.

  • Term-Document Matrix (TDM): A representation of documents in relation to terms, organized in a matrix format.

Text Analytics Techniques

  • Information Extraction: Identifying key phrases and relationships in the text.

    • Example: Named entity recognition (identifying names of people, organizations).

  • Classification: Assigning predefined categories or themes to documents.

  • Clustering: Grouping similar documents without predefined categories.

  • Summarization: Producing concise summaries of documents.

Preprocessing Steps

  1. Term-Document Matrix Creation: Develop a TDM for the corpus using term indices.

  2. Dimensionality Reduction: Remove irrelevant documents and perform filtering operations (e.g., removing stop words or applying stemming).

  3. Frequency Representation: Represent terms using different frequencies:

    • Binary Frequencies: Indicates presence or absence of a term.

    • Raw Frequencies: Counts occurrences of terms in documents.

    • TF-IDF (Term Frequency-Inverse Document Frequency): Measures the importance of terms, balancing term frequency across documents.

    • Normalization: Ensures that the data is appropriately scaled and observed across the matrix.

Sentiment Analysis

  • Definition: Sentiment analysis involves detecting opinions in text data, categorizing them as positive, negative, or neutral, and assessing the strength of those sentiments.

Sentiment Analysis Process

  1. Sentiment Detection: Distinguish between fact and opinion. Filter out objective statements.

    • E.g., Classifying statements based on the presence of subjective adjectives.

  2. Polarity Classification: Classify sentiments into binary classifications (e.g., positive/negative) and measure their strength (e.g., "strongly positive").

    • Challenges arise from sarcasm, slang, and multiple sentiments in texts.

  3. Target Identification: Identify the subject of the sentiment in both short and long texts.

  4. Aggregation: Combine sentiment scores to reflect the overall sentiment for the document.

Advanced Techniques for Polarity Identification

  • Levels of Processing: Analysis can occur at various levels from word to document.

  • Technique Types:

    • Lexicon-based: Utilizing reference libraries for scoring sentiment (manual/semi-automated).

    • Machine Learning: Training models on labeled data to classify sentiments in new documents.

Conclusion

  • Text analytics and sentiment analysis represent powerful tools for understanding and extracting insights from vast amounts of unstructured data, enabling organizations to make informed decisions based on user sentiments and trends.