Semantic Features for Optimizing Supervised Approach of Sentiment Analysis on Product Reviews

被引:7
|
作者
Rintyarna, Bagus Setya [1 ,2 ]
Sarno, Riyanarto [1 ]
Fatichah, Chastine [1 ]
机构
[1] Inst Teknol Sepuluh Nopember, Dept Informat, Surabaya 60111, Indonesia
[2] Univ Muhammadiyah Jember, Dept Elect Engn, Jember 68124, Indonesia
关键词
sentiment analysis; product reviews; machine learning; INFORMATION;
D O I
10.3390/computers8030055
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The growth of ecommerce has triggered online reviews as a rich source of product information. Revealing consumer sentiment from the reviews through Sentiment Analysis (SA) is an important task of online product review analysis. Two popular approaches of SA are the supervised approach and the lexicon-based approach. In supervised approach, the employed machine learning (ML) algorithm is not the only one to influence the results of SA. The utilized text features also handle an important role in determining the performance of SA tasks. In this regard, we proposed a method to extract text features that takes into account semantic of words. We argue that this semantic feature is capable of augmenting the results of supervised SA tasks compared to commonly utilized features, i.e., bag-of-words (BoW). To extract the features, we assigned the correct sense of the word in reviewing the sentence by adopting a Word Sense Disambiguation (WSD) technique. Several WordNet similarity algorithms were involved, and correct sentiment values were assigned to words. Accordingly, we generated text features for product review documents. To evaluate the performance of our text features in the supervised approach, we conducted experiments using several ML algorithms and feature selection methods. The results of the experiments using 10-fold cross-validation indicated that our proposed semantic features favorably increased the performance of SA by 10.9%, 9.2%, and 10.6% of precision, recall, and F-Measure, respectively, compared with baseline methods.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] Sentiment Analysis for Product Features in Chinese Reviews Based on Semantic Association
    Yin, Chunxia
    Peng, Qinke
    [J]. 2009 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, VOL III, PROCEEDINGS, 2009, : 81 - 85
  • [2] Mining Semantic Patterns for Sentiment Analysis of Product Reviews
    Tan, Sang-Sang
    Na, Jin-Cheon
    [J]. RESEARCH AND ADVANCED TECHNOLOGY FOR DIGITAL LIBRARIES (TPDL 2017), 2017, 10450 : 382 - 393
  • [3] Evaluating the performance of sentence level features and domain sensitive features of product reviews on supervised sentiment analysis tasks
    Bagus Setya Rintyarna
    Riyanarto Sarno
    Chastine Fatichah
    [J]. Journal of Big Data, 6
  • [4] Evaluating the performance of sentence level features and domain sensitive features of product reviews on supervised sentiment analysis tasks
    Rintyarna, Bagus Setya
    Sarno, Riyanarto
    Fatichah, Chastine
    [J]. JOURNAL OF BIG DATA, 2019, 6 (01)
  • [5] Sentiment Analysis of Amazon Product Reviews by Supervised Machine Learning Models
    bin Harunasir, Mohamad Faris
    Palanichamy, Naveen
    Haw, Su-Cheng
    Ng, Kok-Why
    [J]. JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY, 2023, 14 (04) : 857 - 862
  • [6] Amazon Product Reviews: Sentiment Analysis Using Supervised Learning Algorithms
    Hawlader, Mohibullah
    Ghosh, Arjan
    Raad, Zaoyad Khan
    Chowdhury, Wali Ahad
    Shehan, Md Sazzad Hossain
    Bin Ashraf, Faisal
    [J]. PROCEEDINGS OF INTERNATIONAL CONFERENCE ON ELECTRONICS, COMMUNICATIONS AND INFORMATION TECHNOLOGY 2021 (ICECIT 2021), 2021,
  • [7] SENTIMENT ANALYSIS ON PRODUCT REVIEWS
    Chauhan, Chhaya
    Sehgal, Smriti
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND AUTOMATION (ICCCA), 2017, : 26 - 31
  • [8] A HYBRID DEEP LEARNING APPROACH FOR SENTIMENT ANALYSIS IN PRODUCT REVIEWS
    Kuang, Minghui
    Safa, Ramin
    Edalatpanah, Seyyed Ahmad
    Keyser, Robert S.
    [J]. FACTA UNIVERSITATIS-SERIES MECHANICAL ENGINEERING, 2023, 21 (03) : 479 - 500
  • [9] SENTIMENT ANALYSIS OF PRODUCT REVIEWS IN THE ABSENCE OF LABELLED DATA USING SUPERVISED LEARNING APPROACHES
    Muhammad, Waqar
    Mushtaq, Maria
    Junejo, Khurum Nazir
    Khan, Muhammad Yaseen
    [J]. MALAYSIAN JOURNAL OF COMPUTER SCIENCE, 2020, 33 (02) : 118 - 132
  • [10] A Study on Sentiment Analysis of Product Reviews
    Parihar, Anil Singh
    Bhagyanidhi
    [J]. IEEE INTERNATIONAL CONFERENCE ON SOFT-COMPUTING AND NETWORK SECURITY (ICSNS 2018), 2018, : 5 - 9