A Review of Unsupervised Approaches of Opinion Target Extraction from  Unstructured Reviews

Khan, Khairullah; Baharudin, Baharum; Khan, Amin

doi:10.19026/rjaset.7.543

Cited by 5 publications

(7 citation statements)

References 24 publications

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“…Its main disadvantage is that it has relatively low classification performance compared to other discriminative algorithms such as support vector machine. However, its simplicity and satisfactory performance makes it more attractive in various tasks [25]. The Naïve Bayes theorem states that given two random variable C and D:…”

Section: Text Mining Algorithmmentioning

confidence: 99%

Analysis of Cyber Bullying on Facebook Using Text Mining

Aliyu

Dogo

Ajibade

et al. 2020

J. Appl. Artif. Intell.

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Cyberbullying is a type of cybercrime that involves the use of the internet and other information technology resources to deliberately insult, embarrass, harass, bully, and threaten people online. The ubiquity of internet connectivity has enabled an increase in the volume and pace of cyberbullying activities because the criminals no longer need to be physically present when committing the crime. This work aims to analyze and predict cyberbullying on Facebook using Naïve Bayes algorithm. The score accuracy, classification report, and confusion matrix are also employed to assess the performance of the classifier. The accuracy of the classifier is 0.95(95%) which means the model can predict 95 of every 100 instances correctly. Also, the result of the experimental analysis shows that Naïve Bayes is effective in classifying a word into a bully or non-bully word and can identify the category of the bully word that is being sent online.

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Section: Text Mining Algorithmmentioning

confidence: 99%

Analysis of Cyber Bullying on Facebook Using Text Mining

Aliyu

Dogo

Ajibade

et al. 2020

J. Appl. Artif. Intell.

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“…Opinion words and opinion targets have been identified in different ways. Some work has purely ,used grammatical structure [30] of the language and some employed semantic features [2] and some has used both syntactic and semantic features [11], [18]. The combined approa,ch has shown proven results.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The WordNet dictionary is a collection of a lengthy lexical database consisting of 117000 synsets. Every synset shows a distinctive idea that is combined with the conceptual-semantic and lexical association [30]. MS Excel is used to generate results and graphs.…”

Section: Precision=mentioning

confidence: 99%

“…Opinion has different components. Identification of each component from free text is a challenging task [3], [4]. This study is about opinion target identification.…”

Section: Introductionmentioning

confidence: 99%

“…The evaluative expression represents a source, an attitude, and a target or destination. For instance, in the sentence 'I disliked the rooms of the hotel, they were not well decorated', the speaker (the source) communicates a negative behavior regarding 'the rooms of a hotel' (the Target) [3].…”

Section: Introductionmentioning

confidence: 99%

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Impact of Anaphora Resolution on Opinion Target Identification

Saqia¹,

Khan²,

Khan³

et al. 2018

ijacsa

Self Cite

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Abstract-Opinion mining is an interesting area of research because of its wide applications in the decision-making process. Opinion mining aims to extract user's perception from the text and to create a fast and accurate summary of people's opinion about anything. In this study, we have worked on opinion target identification and the impact of anaphora resolution on opinion target extraction. Anaphora resolution can be utilized to detect opinion target in sentences having prepositions instead of nouns. We empirically evaluated the impact of anaphora resolution using benchmark datasets. We have achieved accuracy such as precision: 88.14 recall: 71.45 and f-score: 72.12, respectively.

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A taxonomy for sentiment analysis field

Rodrigues

Camilo-Junior

Rosa

2018

IJWIS

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Purpose Due to the large and fast growing sentiment analysis (SA) area recently, many new concepts and different nomenclatures have emerged without the desired organization. This confusion in the research field makes the understandability of the concepts hard and also hampers the comparison of different approaches. Thus, this paper aims to propose a hierarchical taxonomy to help the consolidation of SA area. The taxonomy aims at covering the addressed problems and methods in the SA field. Design/methodology/approach This taxonomy is a filtered union of various classifications found in the literature with a proposal of nomenclatures standardization. As instance, a case study is presented with 20 SA-related articles classified according to the proposed taxonomy. Findings This taxonomy is very expressive because it covers many concepts and is also effective once it allows the distinction and categorization of the previous SA works. Originality/value To the best of the authors’ knowledge, the literature does not present such an expressive and effective classification as proposed in this paper. This new taxonomy allows for the navigation between the existing concepts in the SA field as well as, facilitates the search, comparison and indexing of papers already published.

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A Review of Unsupervised Approaches of Opinion Target Extraction from Unstructured Reviews

Cited by 5 publications

References 24 publications

Analysis of Cyber Bullying on Facebook Using Text Mining

Analysis of Cyber Bullying on Facebook Using Text Mining

Impact of Anaphora Resolution on Opinion Target Identification

A taxonomy for sentiment analysis field

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