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Sentiment Lexicon Adaptation with Context and Semantics for the Social Web
Sentiment Lexicon Adaptation with Context
and Semantics for the Social Web
Information
General purpose sentiment lexicons have been used to compute sentiment from social streams, since they are simple and effective. They calculate the overall sentiment of texts by using a general collection of words, with predetermined sentiment orientation and strength
However, words’ sentiment often vary with the contexts in which they appear, and new words might be encountered that are not covered by the lexicon, particularly in social media environments where content emerges and changes rapidly and constantly
Sentiment analysis over social streams offers governments and organisations a fast and effective way to monitor the publics’ feelings
Lexicon adaptation
Uses contextual as well as semantic information extracted from DBPedia to update the words’ weighted sentiment orientations and to add new words to the lexicon
A general method to adapt sentiment lexicons to any given domain or context, where context is defined by a collection of microblog posts (Tweets)
Context-based Lexicon Adaptation
Semantic Enrichment for Context-based
Lexicon Adaptation
Lexicon-based approaches
These approaches use general-purpose sentiment lexicons (sets of words with associated sentiment scores) to compute the sentiment of a text regardless of its domain or context [4,21,35,14]
Lexicon-based approaches have gained popularity because, as opposed to Machine Learning approaches, they do not require the use of training data, which is often expensive and/or impractical to obtain
However, a word’s sentiment may vary according to the context in which the word is used [36]. For example, the word great conveys different sentiment when associated with the word problem than with the word smile. Therefore, the performance of these lexicons may drop when used to analyse senti
Some works have attempted to address this problem by generating domain-specific lexicons from scratch [2,9,19,17], which tends to be costly, especially when applied to dynamic and generic microblog data (e.g.,[7,10]).
Others opted for extending popular lexicons
to fit new domains [8,16,31,15].
Semantic information
Little attention has been given to the use of semantic information as a resource to perform sentiment lexicon adaptation
For example, the context of the word “Ebola” in “Ebola continues spreading in Africa!” does not indicate a clear sentiment for the word However, “Ebola” is associated with the semantic type (concept) “Virus/Disease”, which suggests that the sentiment of “Ebola” is likely to be negative
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Author
Hassan Saif
Miriam Fernandez
Leon Kastler
Harith Alani
Year
2017
Result