Topic Analysis

Identify main themes and subjects in text using advanced topic modeling techniques.

About: Topic analysis identifies the main themes and subjects in a text, helping to categorize content and understand what it's about.

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Text Processing

Preprocessing

Tokenization

POS

NER

Analysis

Sentiment

Summarization

Topics

Advanced NLP

Generation

Translate

Classify

Embeddings

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Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans. AI research has been defined as the field of study of intelligent agents, which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals. The term "artificial intelligence" had previously been used to describe machines that mimic and display "human" cognitive skills that are associated with the human mind, such as "learning" and "problem-solving". This definition has since been rejected by major AI researchers who now describe AI in terms of rationality and acting rationally, which does not limit how intelligence can be articulated. AI applications include advanced web search engines, recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri or Alexa), self-driving cars, and competing at the highest level in strategic game systems (such as chess and Go). As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. For instance, optical character recognition is frequently excluded from things considered to be AI.

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Topic Analysis Methods

Topic Classification

Identifies predefined topic categories using keyword matching and scoring.

• Environment, Science, Business
• Health, Technology, Politics
• Sports, Entertainment, Travel

Term Frequency

Analyzes word frequencies to identify the most important terms and concepts.

• Word frequency analysis
• Key phrase extraction
• N-gram analysis

TF-IDF Analysis

Identifies distinctive terms using Term Frequency-Inverse Document Frequency.

• Sentence-level analysis
• Distinctive term identification
• Heatmap visualization

LDA Topic Modeling

Uses Latent Dirichlet Allocation to discover abstract topics in text.

• Probabilistic modeling
• Topic distribution
• Network visualization

Analysis Features

Word Clouds

Visual representation of term frequencies

Network Graphs

Topic-term relationship visualization

Heatmaps

TF-IDF term importance visualization

Topic Analysis Results

Click "Analyze Topics" to see topic analysis results