Paper Info
Title
Creating A Social Media-Based Personal Emotional Lexicon
Abstract
One of the major problems when using lexicon in sentiment analysis is that they do not cover all possible words in a text and frequently they miss the more expressive to describe the emotions of the text's author efficiently. This problem occurs because people in non-official, on formal channels, communicate using slangs, neologisms, new patterns based on abbreviations (as "aka", "brb" and "asap") and the different meanings, making challenging to analyse texts using a finite subset of a language. This is a problem because some unknown words can completely change the meaning of a sentence, producing misunderstandings. In this paper we present an approach to expand an emotional lexicon for a specific author, producing a customised lexicon which represents how the author "feels" the words. In our experiments, we got an increase of 35.34% and 107.02% in the dictionary size when compared to the original lexicon using two different authors, and identifying different emotions from the same text according to each author's lexicon, i.e. interpreting the text according to the author's "point of view".
Year
DOI
Venue
2018
10.1145/3243082.3264668
WEBMEDIA'18: PROCEEDINGS OF THE 24TH BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB
Keywords
Field
DocType
Sentiment Analysis, Machine Learning, Natural Processing Language
Social media,Sentiment analysis,Computer science,Lexicon,Neologism,Linguistics,Sentence,Multimedia,AKA
Conference
Citations 
PageRank 
References 
0
0.34
7
Authors
4
Name
Order
Citations
PageRank
Ricardo Martins132.10
José João Almeida26021.83
Paulo Novais3883171.45
Pedro Rangel Henriques427757.91