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Sentiment analysis #
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Traditionally in NLP, sentiment analysis aims to identify the opinion or sentiment of the speaker (writer) toward a certain topic. The increasing availability of massive written tidbits of people everyday life enables the large-scale measurement of people's mood. Peter Sheridan Dodds and Christopher M. Danforth suggested a very simple way to measure happiness from written texts^1. This method was used in Twittermood and several other researches^2. Facebook is also doing similar measurements[^3][^4]. Positive/negative dichotomy is probably too simplistic[^5].

Regarding Homophily and influence, it was shown that the assortativity of happiness can be measured in online social networks[^6][^7].

Interesting application: food mood:

How does sentiment analysis differentiate different domains[^8]?

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Deep learning #

Using word2vec or doc2vec #

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Emoji #

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↑ Peter Sheridan Dodds and Christopher M. Danforth (2010). "Measuring the Happiness of Large-Scale Written Expression: Songs, Blogs, and Presidents". J. Happiness Stud. 11: 441-456. doi:10.1007/s10902-009-9150-9.

↑ Peter Sheridan Dodds et al. (2011). Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter.

↑ "Gross National Happiness". Retrieved February 4, 2011.

↑ Adam D. I. Kramer (March 23, 2010). "How Happy Are We?". Retrieved February 4, 2011.

↑ "Not All Moods are Created Equal! Exploring Human Emotional States in Social Media".

↑ "Crossing Media Streams with Sentiment: Domain Adaptation in Blogs, Reviews and Twitter".

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