Lsislif: CRF and Logistic Regression for Opinion Target Extraction and Sentiment Polarity Analysis

Abstract

This paper describes our contribution in Opinion Target Extraction OTE and Sentiment Polarity sub tasks of SemEval 2015 ABSA task. A CRF model with IOB notation has been adopted for OTE with several groups of features including syntactic, lexical, semantic, sentiment lexicon features. Our submission for OTE is ranked fifth over twenty submissions. A Logistic Regression model with a weighting schema of positive and negative labels have been used for sentiment polarity; several groups of features (lexical, syntactic, semantic, lexicon and Z score) are extracted. Our submission for Sentiment Polarity is ranked third over ten submissions on the restaurant data set, third over thirteen on the laptops data set, but the first over eleven on the hotel data set that is out-of-domain set.

Sentiment Lexicon-Based Features for Sentiment Analysis in Short Text

“…We used L1-regularized Logistic regression classifier implemented in LibLinear [23], this classifier has given good results in recent work [24] [14]. We learned two classifiers one from twitter data set using all features of Section 4 with the three polarities (positive, negative, and neutral) as labels and the second from restaurant review data set using only the following features (word n-gram, negation, lexicon-based, Z score, Brown cluster).…”

SentiSys at SemEval-2016 Task 4: Feature-Based System for Sentiment Analysis in Twitter

“…For example, (Mohammad et al, 2013) used SVM model with several types of features including terms, POS and sentiment lexicons in Twitter data set. (Hamdan et al, 2015a;Hamdan et al, 2015c;Hamdan et al, 2015b) have also proved the importance of feature extraction with logistic regression classifier in Twitter and reviews of restaurants and laptops. They extracted terms, sentiment lexicon and some semantic features like topics.…”

Aspect Based Sentiment Analysis into the Wild

“…Most of the systems dedicated to ABSA use machine learning algorithms such as SVMs (Wagner et al, 2014;Kiritchenko et al, 2014), or CRFs (Toh and Wang, 2014;Hamdan et al, 2015), which are often combined with semantic lexical information, n-gram models, and sometimes more fine-grained syntactic or semantic information. For example, (Kumar et al, 2016) proposed a very efficient system on different languages of SemEval2016.…”