Difference between revisions of "Sentiment Analysis"

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* [[Natural Language Processing (NLP)]]
 
* [[Natural Language Processing (NLP)]]
 
* [http://web.stanford.edu/class/cs124/lec/sentiment2017.pdf Sentiment Analysis | Stanford]
 
* [http://web.stanford.edu/class/cs124/lec/sentiment2017.pdf Sentiment Analysis | Stanford]
* [[Data Augmentation, Data Labeling, and Auto-Tagging]]
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* [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|Data Augmentation, Data Labeling, and Auto-Tagging]]
 
* [http://nlp.stanford.edu/sentiment/ Sentiment Analysis | Stanford’s Sentiment Analysis Demo using Recursive Neural Networks]
 
* [http://nlp.stanford.edu/sentiment/ Sentiment Analysis | Stanford’s Sentiment Analysis Demo using Recursive Neural Networks]
 
* [[News]] analysis and filtering
 
* [[News]] analysis and filtering

Revision as of 19:51, 19 September 2020

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Sentiment Analysis algorithms work by referring external resources where the positive and negative polarity of each word is considered. These external words, for which the polarity is predetermined, are known as lexicons. There are several lists of lexicons available and each one focuses on the polarity of a given word in a particular context. A baseline algorithm for SA using SentiWordNet lexicons Packt


Baseline Algorithms

Sentiment Lexicon

- a database of lexical units for a language along with their sentiment orientations. This can be expressed as a set of tuples of the form (lexical unit, sentiment). Here, the lexical units may be words, word senses, phrases, etc.