A Study on Classification Methods Applied to Sentiment Analysis

Author

Mazzonello, Valentina ; Gaglio, Salvatore ; Augello, Agnese ; Pilato, Giovanni

Author_Institution

DICGIM, Univ. di Palermo, Palermo, Italy

fYear

2013

fDate

16-18 Sept. 2013

Firstpage

426

Lastpage

431

Abstract

Sentiment analysis is a new area of research in data mining that concerns the detection of opinions and/or sentiments in texts. This work focuses on the application and the comparison of three classification techniques over a text corpus composed of reviews of commercial products in order to detect opinions about them. The chosen domain is about "perfumes", and user opinions composing the corpus are written in Italian language. The proposed approach is completely data-driven: a Term Frequency / Inverse Document Frequency (TFIDF) terms selection procedure has been applied in order to make computation more efficient, to improve the classification results and to manage some issues related to the specific classification procedure adopted.

Keywords

classification; data mining; natural language processing; text analysis; Italian language; TFIDF terms selection procedure; classification methods; classification procedure; classification techniques; data mining; opinion detection; sentiment analysis; term frequency/inverse document frequency terms selection procedure; text corpus; Accuracy; Association rules; Context; Feature extraction; Semantics; Training; Vectors; Class Association Rules; Naive Bayes classifier; Random Indexing; Sentiment Classification; TF-IDF;

fLanguage

English

Publisher

ieee

Conference_Titel

Semantic Computing (ICSC), 2013 IEEE Seventh International Conference on

Conference_Location

Irvine, CA

Type

conf

DOI

10.1109/ICSC.2013.82

Filename

6693557