# sentiment analysis

> use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials

**Wikidata**: [Q2271421](https://www.wikidata.org/wiki/Q2271421)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Sentiment_analysis)  
**Source**: https://4ort.xyz/entity/sentiment-analysis

## Summary
Sentiment analysis is the use of natural language processing (NLP), text analysis, and computational linguistics to identify and extract subjective information—such as opinions, emotions, or attitudes—from written or spoken language. It is a key technology in fields like marketing, affective computing, and AI-driven data analysis, enabling systems to interpret human sentiment at scale.

## Key Facts
- **Definition**: Sentiment analysis is a subfield of natural language processing (NLP) and affective computing, focused on detecting subjective information in text.
- **Aliases**: Also known as *opinion mining*, *opinion extraction*, or *sentiment analysis* in multiple languages (e.g., *análisis de sentimiento* in Spanish, *情感分析* in Chinese).
- **Applications**: Used in marketing, customer feedback analysis, social media monitoring, and AI projects like the British Museum’s assessment of visitor content.
- **Tools & Models**:
  - *SentiStrength*: Free academic tool for sentiment analysis (Java/Windows).
  - *VADER (Valence Aware Dictionary for Sentiment Reasoning)*: A polarity-sensitive model for text sentiment analysis.
  - *PolyAnalyst*: Text mining software launched in 1994.
- **Related Fields**: Connected to sarcasm recognition, multimodal sentiment analysis, and computational linguistics.
- **Key Researchers**:
  - *Bing Liu* (b. 1963): Chinese-American computer scientist known for contributions to sentiment analysis.
  - *Katherine Elkins*: Generative AI researcher in sentiment analysis.
  - *Paul Rodrigues*: Computational linguistics researcher and AI director.
- **Classifications**:
  - *MeSH Tree Codes*: Linked to artificial intelligence (G17.035.250.750) and data mining (L01.313.500.750.280.199.750).
  - *ACM Classification Code*: 10003353 (Computing Methodologies).

## FAQs
### Q: What is sentiment analysis used for?
A: Sentiment analysis is primarily used to automatically detect emotions, opinions, or attitudes in text data, helping businesses analyze customer feedback, monitor social media, and improve marketing strategies.

### Q: What are some popular tools for sentiment analysis?
A: Notable tools include *SentiStrength* (free for academics), *VADER* (a polarity-sensitive model), and *PolyAnalyst* (a text mining software since 1994).

### Q: How does sentiment analysis relate to AI?
A: It is a core application of natural language processing (NLP) and affective computing, enabling AI systems to interpret human emotions and subjective language.

### Q: Who are key researchers in sentiment analysis?
A: Influential figures include *Bing Liu* (computer scientist), *Katherine Elkins* (AI researcher), and *Paul Rodrigues* (computational linguist).

### Q: Is sentiment analysis the same as opinion mining?
A: Yes, "opinion mining" is an alias for sentiment analysis, referring to the same process of extracting subjective information from text.

## Why It Matters
Sentiment analysis bridges the gap between raw data and human emotion, allowing organizations to quantify public opinion, customer satisfaction, and social trends at scale. In marketing, it helps brands tailor campaigns based on real-time feedback. In social research, it enables large-scale analysis of public sentiment on political or cultural issues. For AI systems, it enhances human-computer interaction by enabling machines to respond appropriately to emotional cues. By automating the interpretation of subjective language, sentiment analysis reduces the need for manual content moderation and provides actionable insights from unstructured text data—transforming industries from customer service to political polling.

## Notable For
- **Pioneering Tools**: Early software like *PolyAnalyst* (1994) laid the groundwork for modern sentiment analysis.
- **Multilingual Applications**: Recognized globally under various names (e.g., *情感分析* in Chinese, *Sentimentanalyse* in German).
- **Integration with AI**: Critical component of affective computing and NLP, enabling emotional intelligence in machines.
- **Open-Source Models**: Tools like *VADER* and *SentiStrength* provide accessible frameworks for researchers and developers.
- **Cross-Disciplinary Impact**: Used in fields beyond computer science, including museum visitor studies (e.g., British Museum AI project) and literary analysis.

## Body
### **Technical Foundations**
Sentiment analysis combines techniques from:
- **Natural Language Processing (NLP)**: Parsing grammar, syntax, and semantics to understand text.
- **Text Analysis**: Statistical and machine-learning methods to classify sentiment (positive/negative/neutral).
- **Computational Linguistics**: Rule-based or hybrid approaches to detect nuances like sarcasm or irony.

### **Methods and Models**
- **Lexicon-Based**: Uses predefined sentiment dictionaries (e.g., VADER) to score words by polarity.
- **Machine Learning**: Trains models on labeled datasets to predict sentiment (e.g., supervised learning with Naive Bayes or SVM).
- **Hybrid Approaches**: Combines lexicon and ML techniques for higher accuracy, especially in context-dependent cases (e.g., sarcasm).
- **Multimodal Sentiment Analysis**: Extends beyond text to analyze tone of voice, facial expressions, or visual cues.

### **Applications**
- **Business**: Customer feedback analysis, brand monitoring, and product reviews.
- **Social Media**: Tracking public opinion on trends, politics, or viral content.
- **Academia**: Research in linguistics, psychology, and AI (e.g., Katherine Elkins’ work in generative AI).
- **Cultural Institutions**: The British Museum’s AI project uses sentiment analysis to assess visitor-generated content.

### **Challenges**
- **Contextual Nuance**: Sarcasm, irony, and cultural differences can mislead algorithms.
- **Multilingual Support**: Requires language-specific models or translation layers.
- **Data Bias**: Training datasets may reflect demographic or cultural biases, skewing results.

### **Key Tools and Software**
| Tool/Model          | Year   | Key Feature                                                                 |
|---------------------|--------|-----------------------------------------------------------------------------|
| PolyAnalyst         | 1994   | Early text mining software for predictive analytics.                        |
| SentiStrength       | N/A    | Free academic tool for sentiment strength measurement (Java/Windows).      |
| VADER               | N/A    | Open-source model sensitive to polarity and intensity (e.g., emojis, slang).|
| Concraft-Sentipejd  | N/A    | Morphosyntactic tagger with sentiment categories for Polish and other languages. |

### **Research and Development**
- **Bing Liu**: Authored foundational works on opinion mining and sentiment classification.
- **Paul Rodrigues**: Research focuses on AI-driven sentiment analysis in corporate and academic settings.
- **Katherine Elkins**: Explores generative AI’s role in interpreting literary and social sentiment.

## Schema Markup
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  "description": "Use of natural language processing, text analysis, and computational linguistics to identify and extract subjective information in source materials.",
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  "sameAs": [
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    "https://en.wikipedia.org/wiki/Sentiment_analysis"
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  "additionalType": [
    "https://www.wikidata.org/wiki/Q11344",  // natural language processing
    "https://www.wikidata.org/wiki/Q47461344" // affective computing
  ]
}

## References

1. Freebase Data Dumps. 2013
2. UMLS 2023
3. Quora
4. [Source](https://golden.com/wiki/Sentiment_analysis-V48YAR)
5. FactGrid
6. [sentiment-analysis · GitHub Topics · GitHub](https://github.com/topics/sentiment-analysis)
7. [Source](https://vocabs.dariah.eu/tadirah/sentimentAnalysis)
8. [OpenAlex](https://docs.openalex.org/download-snapshot/snapshot-data-format)