TY - GEN
T1 - An unsupervised sentiment classifier on summarized or full reviews
AU - Pera, Maria Soledad
AU - Qumsiyeh, Rani
AU - Ng, Yiu Kai
PY - 2010
Y1 - 2010
N2 - These days web users searching for opinions expressed by others on a particular product or service PS can turn to review repositories, such as Epinions.com or Imdb.com. While these repositories often provide a high quantity of reviews on PS, browsing through archived reviews to locate different opinions expressed on PS is a time-consuming and tedious task, and in most cases, a very labor-intensive process. To simplify the task of identifying reviews expressing positive, negative, and neutral opinions on PS, we introduce a simple, yet effective sentiment classifier, denoted SentiClass, which categorizes reviews on PS using the semantic, syntactic, and sentiment content of the reviews. To speed up the classification process, SentiClass summarizes each review to be classified using eSummar, a single-document, extractive, sentiment summarizer proposed in this paper, based on various sentence scores and anaphora resolution. SentiClass (eSummar, respectively) is domain and structure independent and does not require any training for performing the classification (summarization, respectively) task. Empirical studies conducted on two widely-used datasets, Movie Reviews and Game Reviews, in addition to a collection of Epinions.com reviews, show that SentiClass (i) is highly accurate in classifying summarized or full reviews and (ii) outperforms well-known classifiers in categorizing reviews.
AB - These days web users searching for opinions expressed by others on a particular product or service PS can turn to review repositories, such as Epinions.com or Imdb.com. While these repositories often provide a high quantity of reviews on PS, browsing through archived reviews to locate different opinions expressed on PS is a time-consuming and tedious task, and in most cases, a very labor-intensive process. To simplify the task of identifying reviews expressing positive, negative, and neutral opinions on PS, we introduce a simple, yet effective sentiment classifier, denoted SentiClass, which categorizes reviews on PS using the semantic, syntactic, and sentiment content of the reviews. To speed up the classification process, SentiClass summarizes each review to be classified using eSummar, a single-document, extractive, sentiment summarizer proposed in this paper, based on various sentence scores and anaphora resolution. SentiClass (eSummar, respectively) is domain and structure independent and does not require any training for performing the classification (summarization, respectively) task. Empirical studies conducted on two widely-used datasets, Movie Reviews and Game Reviews, in addition to a collection of Epinions.com reviews, show that SentiClass (i) is highly accurate in classifying summarized or full reviews and (ii) outperforms well-known classifiers in categorizing reviews.
UR - http://www.scopus.com/inward/record.url?scp=78751560026&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17616-6_14
DO - 10.1007/978-3-642-17616-6_14
M3 - Conference contribution
AN - SCOPUS:78751560026
SN - 3642176151
SN - 9783642176159
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 142
EP - 156
BT - Web Information Systems Engineering, WISE 2010 - 11th International Conference, Proceedings
T2 - 11th International Conference on Web Information Systems Engineering, WISE 2010
Y2 - 12 December 2010 through 14 December 2010
ER -