The purpose of this paper is to initially perform Senti-WordNet (SWN)- and point wise mutual information (PMI)-based polarity computation and based polarity updation. When the SWN polarity and polarity mismatched, the vote flipping algorithm (VFA) is employed.

    Design/methodology/approach

    Recently, in domains like social media(SM), healthcare, hotel, car, product data, etc., research on sentiment analysis (SA) has massively increased. In addition, there is no approach for analyzing the positive or negative orientations of every single aspect in a document (a tweet, a review, as well as a piece of news, among others). For SA as well as polarity classification, several researchers have used SWN as a lexical resource. Nevertheless, these lexicons show lower-level performance for sentiment classification (SC) than domain-specific lexicons (DSL). Likewise, in some scenarios, the same term is utilized differently between domain and general knowledge lexicons. While concerning different domains, most words have one sentiment class in SWN, and in the annotated data set, their occurrence signifies a strong inclination with the other sentiment class. Hence, this paper chiefly concentrates on the drawbacks of adapting domain-dependent sentiment lexicon (DDSL) from a collection of labeled user reviews and domain-independent lexicon (DIL) for proposing a framework centered on the information theory that could predict the correct polarity of the words (positive, neutral and negative). The proposed work initially performs SWN- and PMI-based polarity computation and based polarity updation. When the SWN polarity and polarity mismatched, the vote flipping algorithm (VFA) is employed. Finally, the predicted polarity is inputted to the mtf-idf-based SVM-NN classifier for the SC of reviews. The outcomes are examined and contrasted to the other existing techniques to verify that the proposed work has predicted the class of the reviews more effectually for different datasets.

    Findings

    There is no approach for analyzing the positive or negative orientations of every single aspect in a document (a tweet, a review, as well as a piece of news, among others). For SA as well as polarity classification, several researchers have used SWN as a lexical resource. Nevertheless, these lexicons show lower-level performance for sentiment classification (SC) than domain-specific lexicons (DSL). Likewise, in some scenarios, the same term is utilized differently between domain and general knowledge lexicons. While concerning different domains, most words have one sentiment class in SWN, and in the annotated data set their occurrence signifies a strong inclination with the other sentiment class.

    Originality/value

    The proposed work initially performs SWN- and PMI-based polarity computation, and based polarity updation. When the SWN polarity and polarity mismatched, the vote flipping algorithm (VFA) is employed.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    PMI-based polarity computation for SVM-NN-based sentiment classification from user-generated reviews


    Weitere Titelangaben:

    SWN and PMI


    Beteiligte:


    Erscheinungsdatum :

    07.01.2022


    Format / Umfang :

    21 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Sentiment analysis based product rating using textual reviews

    Sindhu, C / Vyas, Dyawanapally Veda / Pradyoth, Kommareddy | IEEE | 2017


    SENTIMENT-BASED AUTONOMOUS VEHICLE USER INTERACTION AND ROUTING RECOMMENDATIONS

    LEARY JESSICA / ZINITI CECILIA | Europäisches Patentamt | 2022

    Freier Zugriff

    Sentiment-based autonomous vehicle user interaction and routing recommendations

    ZINITI CECILIA / LEARY JESSICA | Europäisches Patentamt | 2022

    Freier Zugriff

    Sentiment-based autonomous vehicle user interaction and routing recommendations

    LEARY JESSICA / ZINITI CECILIA | Europäisches Patentamt | 2024

    Freier Zugriff

    A teaching evaluation method based on sentiment classification

    Zhao, Hua / Ji, Xiaowen / Zeng, Qingtian et al. | British Library Online Contents | 2016