Sentiment analysis

sentimentopinion mininganalysisassess the sentimentassessing how positivesemantic and sentiment analysissentiment identificationsentiment miningSentimentDetector
Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.wikipedia
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Text mining

text analyticsTextminingtext and data mining
Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.
Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities).

Natural language processing

NLPnatural languagenatural-language processing
Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.

Multimodal sentiment analysis

Sentiment analysis can also be performed on visual content, i.e., images and videos (see Multimodal sentiment analysis).
Multimodal sentiment analysis is a new dimension of the traditional text-based sentiment analysis, which goes beyond the analysis of texts, and includes other modalities such as audio and visual data.

Deep learning

deep neural networkdeep neural networksdeep-learning
The automatic identification of features can be performed with syntactic methods, with topic modeling, or with deep learning. Statistical methods leverage elements from machine learning such as latent semantic analysis, support vector machines, "bag of words", "Pointwise Mutual Information" for Semantic Orientation, and deep learning.
Deep neural architectures provide the best results for constituency parsing, sentiment analysis, information retrieval, spoken language understanding, machine translation, contextual entity linking, writing style recognition, Text classification and others.

Machine learning

machine-learninglearningstatistical learning
Statistical methods leverage elements from machine learning such as latent semantic analysis, support vector machines, "bag of words", "Pointwise Mutual Information" for Semantic Orientation, and deep learning.

Market sentiment

bullishbearishsentiment
In the last decade, investors are also known to measure market sentiment through the use of news analytics, which include sentiment analysis on textual stories about companies and sectors.

Recommender system

recommender systemsrecommendation systemrecommendation systems
For a recommender system, sentiment analysis has been proven to be a valuable technique.
Popular approaches of opinion-based recommender system utilize various techniques including text mining, information retrieval, sentiment analysis (see also Multimodal sentiment analysis) and deep learning.

Computational linguistics

mathematical linguisticscomputational linguistSymbolic Systems
Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.

Biometrics

biometricbiometric authenticationbiometric data
Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.

Voice of the customer

voice of customerVOCCustomer Needs
Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.

Marketing

marketermarketedmarketing campaign
Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.

Customer relationship management

CRMcustomer relationscustomer-relationship management
Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.

Word order

free word orderConstituent orderbasic word order
In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, grammar and even word order.

Happiness

happyenjoymentJolly
Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as "angry", "sad", and "happy ".

Mental state

mental statespsychological statecognitive state
Precursors to sentimental analysis include the General Inquirer, which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's psychological state based on analysis of their verbal behavior.

Review

reviewsreviewerproduct review
Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney, and Pang who applied different methods for detecting the polarity of product reviews and movie reviews respectively.

Maximum entropy probability distribution

maximum entropymaximum entropy distributionlargest entropy
Moreover, it can be proven that specific classifiers such as the Max Entropy and SVMs can benefit from the introduction of a neutral class and improve the overall accuracy of the classification.

Naive Bayes classifier

Naive Bayesnaive Bayes classificationNaïve Bayes
This second approach often involves estimating a probability distribution over all categories (e.g. naive Bayes classifiers as implemented by the NLTK).

Natural Language Toolkit

NLTK
This second approach often involves estimating a probability distribution over all categories (e.g. naive Bayes classifiers as implemented by the NLTK).