Overview
This lecture introduces sentiment analysis and semantic analysis, explains their importance in business, and discusses techniques and challenges in analyzing text data for emotional tone and meaning.
Sentiment Analysis: Definition and Applications
- Sentiment analysis uses natural language processing (NLP) to determine if text expresses positive, negative, or neutral opinions.
- It quantifies the overall sentiment in customer reviews, social media, and other text data automatically.
- Businesses use sentiment analysis to understand customer opinions about products, services, or promotions.
- Automated sentiment analysis eliminates the need for manual review of large volumes of text.
Techniques and Challenges in Sentiment Analysis
- A basic approach is dictionary-based, where positive and negative words are tallied to determine sentiment.
- Challenges include detecting subjectivity, handling conditional sentences, sarcasm, contextual word meanings, and pronoun resolution.
- Lexicons may need to be domain-specific, as word sentiment can vary by context.
Decision Support Systems Using Sentiment Analysis
- Sentiment analysis can be integrated into systems to compare customer opinions across competitors and product categories.
- Example: A system for retailers like Costco analyzes online customer feedback and summarizes competitive sentiment by product category.
- This helps businesses identify strengths and weaknesses in their offerings relative to competitors.
Semantic Analysis: Beyond Emotional Tone
- Semantic analysis focuses on understanding the meaning of text, not just the sentiment.
- Categorical approaches use dictionaries of related words (e.g., university, health, money) to identify topics in documents.
- LIWC (Linguistic Inquiry and Word Count) categorizes words across over 100 topics and provides proportions for each in a text.
Hierarchical Semantic Networks
- WordNet is a system that organizes all words into a hierarchical network based on their meanings and relationships.
- Words are classified from broad categories (e.g., physical object) down to specific items (e.g., types of dogs).
- This hierarchy enables finding synonyms and understanding word relationships for deeper semantic analysis.
Key Terms & Definitions
- Sentiment Analysis — Automated detection of positive, negative, or neutral opinions in text.
- Semantic Analysis — Process of determining the meaning or topics present in a text.
- Lexicon — A dictionary of words labeled by sentiment or topic.
- LIWC — Software tool for analyzing text by categorizing words into psychological and topical categories.
- WordNet — Hierarchical lexical database that relates words by meaning and organizes them into a semantic network.
Action Items / Next Steps
- Review how to build and use lexicons for sentiment analysis.
- Explore the use of tools like LIWC and WordNet for semantic analysis.
- Prepare to discuss how themes are extracted from online postings in the next session.