Wikipedia: Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials.

In 1997, firstly proposed by MIT Professor Rosalind Picard in Effective Computing.

Basic task is classifying the polarity (positive, negative, or neutral). beyond polarity: e.g. emotional states such angry, sad, and happy. Higher: sarcasm?

Application and methodologies:

  • Product, restaurant, or film review rating prediction.

Most statistical classification methods ignore the neutral class.

  • Use a scaling system, give the words an associated number on a -10 to +10 scale.
  • Another direction is subjective/objective identification. Remove objective sentences improve the polarity classification performance.
  • A more fine-grained analysis model is called the feature/aspect-based sentiment analysis.

Chinese Sentiment Dictionary and Resources

Stanford NLP cource on Coursera (Class 7) --> Sentiment Analysis introduced the resources and methods

Query:

Whose sentiment is able to predict the stock movement better? How to find out them?

Sentiment Analysis resources的更多相关文章

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    In this lesson, Andrew Trask, the author of Grokking Deep Learning, will walk you through using neur ...

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  4. Sentiment analysis in nlp

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  6. 论文翻译——Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

    Abstract Semantic word spaces have been very useful but cannot express the meaning of longer phrases ...

  7. Standford CoreNLP--Sentiment Analysis初探

    Stanford CoreNLP功能之一是Sentiment Analysis(情感分析),可以标识出语句的正面或者负面情绪,包括:Positive,Neutral,Negative三个值. 运行有两 ...

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