Learning domain-specific and domain-independent opinion oriented lexicons using multiple domain knowledge

Vishnu, K.; Apoorva, Tirumalasetty Sri Sai; Gupta, Deepa

doi:10.1109/ic3.2014.6897193

Cited by 10 publications

(2 citation statements)

References 14 publications

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“…Despite the popularity of DSA across diverse topics of public health research, few studies in the public health context assess the validity of DSA in comparison to the manual coding approach, which is a well-established sentiment classification method. Some evaluation studies applying DSA to public health topics questioned the validity of DSA, particularly domain-independent applications, by showing their low-performance scores [ 8 , 9 ]. In other domains such as economics, a previous study explicitly concluded that DSA is invalid compared to the manual coding approach [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Detecting Sentiment toward Emerging Infectious Diseases on Social Media: A Validity Evaluation of Dictionary-Based Sentiment Analysis

Lee

Meng

et al. 2022

IJERPH

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Despite the popularity and efficiency of dictionary-based sentiment analysis (DSA) for public health research, limited empirical evidence has been produced about the validity of DSA and potential harms to the validity of DSA. A random sample of a second-hand Ebola tweet dataset was used to evaluate the validity of DSA compared to the manual coding approach and examine the influences of textual features on the validity of DSA. The results revealed substantial inconsistency between DSA and the manual coding approach. The presence of certain textual features such as negation can partially account for the inconsistency between DSA and manual coding. The findings imply that scholars should be careful and critical about findings in disease-related public health research that use DSA. Certain textual features should be more carefully addressed in DSA.

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Section: Introductionmentioning

confidence: 99%

Detecting Sentiment toward Emerging Infectious Diseases on Social Media: A Validity Evaluation of Dictionary-Based Sentiment Analysis

Lee

Meng

et al. 2022

IJERPH

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“…Consequently, research efforts have lately been shifted toward constructing a DSL to resolve this issue. The acquisition of this lexicon could be done in (1) manual approach, (2) corpus-based approach (CBA) and (3) dictionary-based approaches (Vishnu et al , 2014). With the knowledge of multiple domains, most approaches learned the domain-independent lexicons (DILs) and DSLs utilizing the CBA (Hammer et al , 2015).…”

Section: Introductionmentioning

confidence: 99%

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

Padmavathy

Mohideen

Gulzar

2021

IJIUS

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PurposeThe 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/approachRecently, 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.FindingsThere 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/valueThe 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.

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Edge Network Extension Based on Multi-domains Fusion and LEO Satellite

Ren

Hou

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Learning domain-specific and domain-independent opinion oriented lexicons using multiple domain knowledge

Cited by 10 publications

References 14 publications

Detecting Sentiment toward Emerging Infectious Diseases on Social Media: A Validity Evaluation of Dictionary-Based Sentiment Analysis

Detecting Sentiment toward Emerging Infectious Diseases on Social Media: A Validity Evaluation of Dictionary-Based Sentiment Analysis

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

Edge Network Extension Based on Multi-domains Fusion and LEO Satellite

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