TY - GEN
T1 - Opinion analysis in social networks using antonym concepts on graphs
AU - Calvo, Hiram
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - In sentiment analysis a text is usually classified as positive, negative or neutral; in this work we propose a method for obtaining the relatedness or similarity that an opinion about a particular subject has with regard to a pair of antonym concepts. In this way, a particular opinion is analyzed in terms of a set of features that can vary depending on the field of interest. With our method, it is possible, for example, to determine the balance of honesty, cleanliness, interestingness, or expensiveness that is expressed in an opinion. We used the standard similarity measures Hirst-St-Onge, Jiang-Conrath and Resnik from WordNet; however, finding that these measures are not well-suitable for working with all Parts-of-Speech, we additionally proposed a new measure based on graphs, to properly handle adjectives. We validated our results with a survey to a sample of 20 individuals, obtaining a precision above 82 % with our method.
AB - In sentiment analysis a text is usually classified as positive, negative or neutral; in this work we propose a method for obtaining the relatedness or similarity that an opinion about a particular subject has with regard to a pair of antonym concepts. In this way, a particular opinion is analyzed in terms of a set of features that can vary depending on the field of interest. With our method, it is possible, for example, to determine the balance of honesty, cleanliness, interestingness, or expensiveness that is expressed in an opinion. We used the standard similarity measures Hirst-St-Onge, Jiang-Conrath and Resnik from WordNet; however, finding that these measures are not well-suitable for working with all Parts-of-Speech, we additionally proposed a new measure based on graphs, to properly handle adjectives. We validated our results with a survey to a sample of 20 individuals, obtaining a precision above 82 % with our method.
KW - Adjective similarity measure
KW - Antonyms
KW - Opinion mining
KW - Sentiment analysis
KW - Wordnet
UR - http://www.scopus.com/inward/record.url?scp=84951978660&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-26135-5_9
DO - 10.1007/978-3-319-26135-5_9
M3 - Contribución a la conferencia
SN - 9783319261348
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 109
EP - 120
BT - Future Data and Security Engineering - 2nd International Conference, FDSE 2015, Proceedings
A2 - Takizawa, Makoto
A2 - Neuhold, Erich
A2 - Dang, Tran Khanh
A2 - Thoai, Nam
A2 - Wagner, Roland
A2 - Küng, Josef
PB - Springer Verlag
T2 - 2nd International Conference on Future Data and Security Engineering, FDSE 2015
Y2 - 23 November 2015 through 25 November 2015
ER -