Sentiment analysis

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Sentiment analysis topic overview

The analysis highlights Applications, Methods and features and Types as prominent areas in the source structure around Sentiment analysis.

Related topics: 57
Source areas: 6
Connected nodes: 63
Extracted relationships: 124
Concept neighborhoods: 18
Bridge connections: 63

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Methods and features · 15 topics

Types · 13 topics

Web 2.0 · 10 topics

Overview · 8 topics

Application in recommender systems · 6 topics

Evaluation · 5 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

Natural language processing
Text analysis Text analytics
Computational linguistics
Biometrics
Voice of the customer
Marketing
Customer service Customer relationship management
RoBERTa

Types

Psychological state
Product reviews Product review
AAAI
Max Entropy Maximum entropy probability distribution
SVMs Support vector machine
Naive Bayes Naive Bayes classifier
NLTK
Unsupervised machine learning Unsupervised learning
Topic modeling Topic model
Deep learning
Ensemble Ensemble learning
Convolutional neural networks Convolutional neural network
Gated recurrent units Gated recurrent unit

Methods and features

Evaluation

Web 2.0

Application in recommender systems

Recommender system
Collaborative filtering
Content-based filtering
Meta-data Metadata
Social networking services Social networking service
E-commerce

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Sentiment analysis connects Entity context

The extracted context around Sentiment analysis shows recurring relationship patterns in the source. For example, Sentiment analysis → Approaches, Existing, Grammatical, Hybrid, In, Knowledge-based, More, Multimodal, One, Open, Pointwise Mutual Information, Semantic Orientation, SentiBank, Sentiment, Some, Statistical, The, To Another extracted example is Sentiment analysis → Awareness, Carbonell, However, Moreover, Pang, Su, Subjective, The, This, Yale University. Use these groups to spot repeated connection types before inspecting the individual relationships.

Top relations

has method · 18

Sentiment analysis → Approaches, Existing, Grammatical, Hybrid, In, Knowledge-based, More, Multimodal, One, Open, Pointwise Mutual Information, Semantic Orientation, SentiBank, Sentiment, Some, Statistical, The, To

related to Subjectivity/objectivity identification · 10

Sentiment analysis → Awareness, Carbonell, However, Moreover, Pang, Su, Subjective, The, This, Yale University

related to Application in recommender systems · 9

Sentiment analysis → Also, For, In, Mainstream, Potentially, Since, The, These, Users

related to Ethical considerations · 8

Sentiment analysis → Ethical, Furthermore, Industrial Valorisation Advisory Boards, Issues, SEWA, Such, The, These

related to Web 2.0 · 8

Sentiment analysis → As, Further, If, One, Reddit, Several, The, With

related to Evaluation · 7

Sentiment analysis → For, However, In, Inter-rater, On, The, This

related to Feature/aspect-based · 6

Sentiment analysis → Different, It, Liu's, More, The, This

related to Types · 3

Sentiment analysis → Advanced, General Inquirer, Precursors

is a · 1

Sentiment analysis → possibility to capture nuances about objects of interest

see also · 1

Sentiment analysis → Affective

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

sentiment analysis text subjective words positive data features negative based one objective methods also learning may information language task level

Sentiment analysis relationships Subject–Predicate–Object triples

TTTA extracted 124 structured relationships around Sentiment analysis. Examples in this analysis include Sentiment analysis → is a → possibility to capture nuances about objects of interest and reviews → instance of → and study affective states and subjective information.Sentiment analysis is widely applied to voice of the customer materials. The table shows each extracted connection, where it came from and its confidence.

Subject	Predicate	Object	Confidence	Src
Sentiment analysis	is a	possibility to capture nuances about objects of interest	0.90	text
reviews	instance of	and study affective states and subjective information.Sentiment analysis is widely applied to voice of the customer materials	0.80	text
survey responses	instance of	and study affective states and subjective information.Sentiment analysis is widely applied to voice of the customer materials	0.80	text
online	instance of	and study affective states and subjective information.Sentiment analysis is widely applied to voice of the customer materials	0.80	text
social media	instance of	and study affective states and subjective information.Sentiment analysis is widely applied to voice of the customer materials	0.80	text
and healthcare materials for applications that range from marketing to customer service to clinical medicine	instance of	and study affective states and subjective information.Sentiment analysis is widely applied to voice of the customer materials	0.80	text
enjoyment	instance of	at emotional states	0.80	text
anger	instance of	at emotional states	0.80	text
disgust	instance of	at emotional states	0.80	text
sadness	instance of	at emotional states	0.80	text
fear	instance of	at emotional states	0.80	text
and surprise.Precursors to sentimental analysis include the General Inquirer	instance of	at emotional states	0.80	text

Related concept clusters Concept neighborhoods

The concept neighborhoods around Sentiment analysis bring nearby vocabulary together. In this analysis, examples include Sentiment, Positive and Also. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

Sentiment analysis
- Sentiment
- Positive
- Also
- Text
- Negative
- Media
- Given
- Social
- Opinion
- Methods
- Based
- Language
sentiment analysis
- Sentiment
- Media
- Social
- Positive
- Also
- Text
- Reviews
- Information
- Negative
- Given
- Opinion
- Methods
text analysis
- Sentiment
- Media
- Social
- Also
- Reviews
- Information
- Sentiments
- Opinion
- Words
- Research
- Language
- Learning
latent semantic analysis
- Sentiment
- Media
- Social
- Also
- Reviews
- Information
- Opinion
- Research
- Language
- Learning
- Methods
- Negative
bag of words
- Based
- Patterns
- Language
- Text
- Subjective
- Given
- Different
- Researchers
- Level
- Sentiment
- Methods
- Objective
multimodal sentiment analysis
- Sentiment
- Media
- Social
- Positive
- Also
- Text
- Reviews
- Information
- Negative
- Given
- Opinion
- Methods
data mining
- Text
- Two
- Language
- Neutral
- Task
- Learning
- Methods
- Based
- Negative
- Sentiment
- Positive
- Media
methods and features
- Learning
- Items
- Sentiments
- Polarity
- Item
- Different
- Opinion
- Subjective
- Reviews
- Neutral
- Opinions
- Sentiment

Connections between topic areas Semantic bridges

For Sentiment analysis, one of the stronger structural bridges in this analysis connects Sentiment analysis with Methods and features. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3

Sentiment analysis — Methods and features · splits 48 ⟂ 16

Sentiment analysis — Types · splits 50 ⟂ 14

Sentiment analysis — Web 2.0 · splits 53 ⟂ 11

Sentiment analysis — Overview · splits 55 ⟂ 9

Sentiment analysis — Application in recommender systems · splits 57 ⟂ 7

Sentiment analysis — Evaluation · splits 58 ⟂ 6

Map overview Semantic statistics

Sentiment analysis

Nodes64

Edges63

Triples124

Avg. degree1.97

Density0.03125

Components1

Source & methodology

TTTA analyzes the structure around Sentiment analysis to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Applications, Methods and features & Types, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Sentiment analysis · EN edition · Analysis: TopicsToTalkAbout

Sentiment analysis: Applications, Methods and features & Types

Sentiment analysis topic overview

What this topic covers Research coverage

Key facts & relationships

Explore all related topics Closing gaps

Overview

Types

Methods and features

Evaluation

Web 2.0

Application in recommender systems

Advanced semantic analysis

How Sentiment analysis connects Entity context

Sentiment analysis

Top relations

Important terminology

Important terminology

Sentiment analysis relationships Subject–Predicate–Object triples

Related concept clusters Concept neighborhoods

Connections between topic areas Semantic bridges

Map overview Semantic statistics

Sentiment analysis

Source & methodology