Methods for Identifying Comparative Sentences

Saritha, Sri Khetwat; Pateriya, R. K.

doi:10.5120/19020-0504

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Saritha and Pateriya [15] studied various methods for comparative sentence identification and classification. They found that few studies had been conducted on comparative sentences, and they used different supervised and unsupervised techniques to identify the comparative sentences and relations.…”

Section: Text Classificationmentioning

confidence: 99%

Identifying comparative opinions in Arabic text in social media using machine learning techniques

Alharbi

Khan

2019

SN Appl. Sci.

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Social networking sites have become an integral part of everyday life, where people interact, cooperate and quarrel with each other. Social media also encourages them to express their opinions and share their comments about their lives' events or about the product they use. Opinions can be direct without any comparison (I like ABC phone) or they can be comparative (X-phone's camera is better than Y-phone). Comparative opinions are useful in many applications, e.g. marketing intelligence, product benchmarking, and e-commerce. The automatic mining of comparative opinions is an important text mining problem and an area of increasing interest for different languages. This paper focuses on identification of comparative sentence from non-comparative ones in Arabic texts. A corpus was developed consisting of YouTube comments. This paper describes research experiments that aimed to apply data/text mining algorithms, natural language processing and linguistic classification for Arabic comparative text discovery. The results of these experiments along with the analysis are also presented. The results were promising reaching to 91% accuracy for the detection of comparative opinions.

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Section: Text Classificationmentioning

confidence: 99%

Identifying comparative opinions in Arabic text in social media using machine learning techniques

Alharbi

Khan

2019

SN Appl. Sci.

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“…Some studies also used machine learning method to carry out experiments [8], [9]. Saritha et al [10] summarized the techniques used to identify the comparative sentences from text documents. From pattern matching to machine learning, the results of Jindal et al revealed that most of the techniques still need numerous manual annotations, and their performance requires further improvement.…”

Section: Comparative Sentences Identificationmentioning

confidence: 99%

Improving Comparative Sentence Extraction of Chinese Product Reviews by Sentiment Analysis

Shi¹,

Li²,

Jiang³

et al. 2016

JESTR

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Comparative sentences in online reviews express substantial information that are of concern to customers and attract increasing attention from manufacturers and service providers. At present studies of Chinese Comparative Sentence Identification are based on Pattern Matching and Supervised Machine Learning Algorithm, in which its performance requires further improvement. Therefore, the present analysis aims to further identify the candidate sets of comparative sentences by unsupervised sentiment analysis, and then improve mining performance. First, we constructed a Chinese comparative pattern set, which was used to extract candidate comparative sentences from the corpus of online products reviews. Moreover, we set the score for the candidate sentences using sentiment analysis technique. The experiment determined the threshold of positive and negative affective means, ranging from 0 to 0.03. Experimental results on Chinese customer reviews show that the final F-score value increased to 87.54%. In addition, a significant difference was set at the 0.01 level, which demonstrates the effectiveness of the technique. The proposed unsupervised method is suitable for the changeable and the large quantity of network review mining. This study does not only meet the need to generalize across different products and various data sizes but also improves the performance in terms of identifying comparative sentences.

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“…SVM (support vector machine) is one of text classification methods that is known to have high performance among many other classifiers [4]- [6]. On the other hand, Naïve Bayes is a classifier that is a simple and easy to implement [7].…”

Section: Introduction a Backgroundmentioning

confidence: 99%

Effectiveness of SVM Method by Naïve Bayes Weighting in Movie Review Classification

Zain

Sibaroni

2019

khif

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Classification of movie review belongs to the domain of text classification, particularly in the field of sentiment analysis. Popular text classification methods for the process include Support Vector Maching (SVM) and Naïve Bayes. Both methods are known to have good performance in handling text classification individually separately. Combination of the two may be expected to improve the classification performance compared to the performance of each individual method. This paper reports an effort to classify movie review using the combined method of SVM with Naïve Bayes as the weighting factor, which is commonly called NBSVM. Our work shows that higher accuracy is obtained when classification is done using NBSVM rather than using individual methods. Accuracy at the level of 88.8% is attained when using the combined feature of unigram and bigram with only data cleansing in the pre-processing stage.

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Methods for Identifying Comparative Sentences

Cited by 6 publications

References 5 publications

Identifying comparative opinions in Arabic text in social media using machine learning techniques

Identifying comparative opinions in Arabic text in social media using machine learning techniques

Improving Comparative Sentence Extraction of Chinese Product Reviews by Sentiment Analysis

Effectiveness of SVM Method by Naïve Bayes Weighting in Movie Review Classification

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