Authors:
Mhairi McNeill
1
;
Robert Raeside
2
;
Martin Graham
2
and
Isaac Roseboom
3
Affiliations:
1
Edinburgh Napier University and deltaDNA, United Kingdom
;
2
Edinburgh Napier University, United Kingdom
;
3
deltaDNA, United Kingdom
Keyword(s):
Latent Dirichlet Allocation, Natural Language Processing, Opinion Extraction, Review Summarisation, Sentiment Analysis, Text Mining.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Mining Text and Semi-Structured Data
;
Symbolic Systems
Abstract:
In this paper we evaluate three methods for summarising game reviews written in a casual style. This was
done in order to create a review summarisation system to be used by clients of deltaDNA. We look at one
well-known method based on natural language processing, and describe two statistical methods that could be
used for summarisation: one based on TF-IDF scores another using supervised latent Dirichlet allocation. We
find, due to the informality of these online reviews, that natural language based techniques work less well
than they do on other types of reviews, and we recommend using techniques based on the statistical properties
of the words’ frequencies. In particular, we decided to use a TF-IDF score based system in the final system.