Authors:
Adolfo Flores Moreno
;
Silvia B. González-Brambila
and
Juan G. Vargas-Rubio
Affiliation:
Universidad Autónoma Metropolitana-Azcapotzalco, Mexico
Keyword(s):
Violence, Classification, Data Mining.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Mining Multimedia Data
;
Mining Text and Semi-Structured Data
;
Process Mining
;
Symbolic Systems
Abstract:
Violent behavior in our society has been studied from many points of view, yet many cause-effect relations remain unexplained. Security personnel are normally trained to be alert and recognize potential violent behavior, but they cannot be 100% effective in recognizing it due to the monotonous nature of their job. This paper presents the first results of a work in progress detecting violence from the analysis of words in conversations. We used a set of videos with two person conversations in Spanish and classified them as violent and non violent. The audio of the conversations was extracted and converted to text. We used “Ward”, “K-means” and “PAM” (clValid, 2014) to group words, performing a clValid analysis we found that the hierarchical technique was the best. The percentages of frequency were computed for each term and the SVM (Meyer, 2014) technique was applied, from which we found that there were unclassifiable terms. In three of the tests the prediction was erroneous and in an
other three we obtained good predictions with respect to the test set.
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