WordNet based Implicit Aspect Sentiment Analysis for Crime Identification from Twitter

Hannach, Hajar El; Benkhalifa, Mohammed

doi:10.14569/ijacsa.2018.091222

Cited by 16 publications

(7 citation statements)

References 18 publications

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“…We can see that the hybrid approach using the NLP's model BERT gives better results than the lexicon-based and machine learning approaches in both accuracy and F1-score metrics, as BERT outperform the other NLP's model in context understanding part, especially in the "context heavy" one, for which it is so important for analytics purposes. Lexicon-based -55% [30] Machine Learning-MNB -83% Machine Learning-SVM -89% Machine Learning-RF -87% [31] Lexicon-based 93% -…”

Section: Evaluating Our Modelmentioning

confidence: 99%

Crime prediction using a hybrid sentiment analysis approach based on the bidirectional encoder representations from transformers

Boukabous¹,

Azizi²

2022

IJEECS

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Sentiment analysis (SA) is widely used today in many areas such as crime detection (security intelligence) to detect potential security threats in realtime using social media platforms such as Twitter. The most promising techniques in sentiment analysis are those of deep learning (DL), particularly bidirectional encoder representations from transformers (BERT) in the field of natural language processing (NLP). However, employing the BERT algorithm to detect crimes requires a crime dataset labeled by the lexiconbased approach. In this paper, we used a hybrid approach that combines both lexicon-based and deep learning, with BERT as the DL model. We employed the lexicon-based approach to label our Twitter dataset with a set of normal and crime-related lexicons; then, we used the obtained labeled dataset to train our BERT model. The experimental results show that our hybrid technique outperforms existing approaches in several metrics, with 94.91% and 94.92% in accuracy and F1-score respectively.

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Section: Evaluating Our Modelmentioning

confidence: 99%

Crime prediction using a hybrid sentiment analysis approach based on the bidirectional encoder representations from transformers

Boukabous¹,

Azizi²

2022

IJEECS

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“…Even though the model can utilize minimum data required to capture the aspect oriented sentiment, but stressful in identifying the appropriate feature for optimal extraction. Author [107] used lexicon-based approach that is based on term-weighting scheme and WordNet semantic relations, to improve training data crime implicit aspect sentences detection as well as crime implicit aspect extraction.…”

Section: Lexicon-basedmentioning

confidence: 99%

A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis

et al. 2020

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Aspect-based sentiment analysis (ABSA) is currently among the most vigorous areas in natural language processing (NLP). Individuals, private and government institutions are increasingly using media sources for decision making. In the last decade, aspect extraction has been the most essential phase of sentiment analysis (SA) to conduct an abridged sentiment classification. However, previous studies on sentiment analysis mostly focused on explicit aspects extraction with limited work on implicit aspects. To the best of our knowledge, this is the first systematic review that covers implicit, explicit, and the combination of both implicit and explicit aspect extractions. Therefore, this systematic review has been conducted to, 1) identify techniques used for extracting implicit, explicit, or both implicit and explicit aspects; 2) analyze the various evaluation metrics, data domains, and languages involved in the implicit and explicit aspect extraction in sentiment analysis from years 2008 to 2019; 3) identify the key challenges associated with the techniques based on the result of a comprehensive comparative analysis; and finally, 4) highlight the feasible opportunities for future research directions. This review can be used to assist novice and prominent researchers to understand the concept of both implicit and explicit aspect extractions in aspect-based sentiment analysis domain. INDEX TERMS aspect-based sentiment analysis, aspect extraction, explicit aspect, feature extraction, implicit aspect.

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“…For hidden sentiment, the extraction is based on the findings shown in [10] that consider different synonym and definition subsets for adjective, adverb and verb. For adjective and adverb, the synonyms used are extracted from the definition relation (D) while the part of synonyms appearing in D contains more reliable terms with closer meaning than terms appearing in Synonym relations [10]. For the potential verbs that imply hidden sentiment we extract terms appearing only in definition and not in synonyms (D-S).…”

Section: Semantic Lists Creationmentioning

confidence: 99%

“…For the potential verbs that imply hidden sentiment we extract terms appearing only in definition and not in synonyms (D-S). Verbs have many synonyms with different contexts that lead to poor results when considering all of them [10].…”

Section: Semantic Lists Creationmentioning

confidence: 99%

“…At the first step, synonyms are considered to enhance training data for majority and minority classes. For hidden sentiment extraction, we investigated the findings shown in [10] that consider different synonym and definition subsets for adjective, adverb and verb. At the second step, the antonyms are used to support the minority class based on their distribution on the majority class.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Enhanced MNB Based Model for Explicit and Hidden Sentiment Classification in Imbalanced Datasets

Hanhach¹,

Benkhalifa²

2019

IJIES

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Sentiment polarity classification (either explicit or hidden) is the process by which information can be extracted to be analysed as positive or negative opinion. Much work on supervised machine learning based sentiment classification has been done considering balanced datasets. However, due to the imbalanced nature of data distribution, sentiment classification becomes a complex task that requires investigating more efficient approach, especially for hidden sentiment. Multinomial Naïve Bayes (MNB) algorithm is one of the most widely used methods in this field, due to its computational efficiency and relatively good predictive performance. However, this classifier performs poorly on imbalanced datasets. In this paper, we propose transformation in MNB prior and conditional probabilities designed to handle explicit and hidden sentiment classification for imbalanced data. To support MNB, we introduce an original frequency model based on using synonym and antonym semantic relations. Our approach is empirically evaluated according to: (1) its impact on MNB training data quality for minority classes, (2) its comparison to similar works involving MNB for imbalanced datasets and (3) its comparison to commonly used classifiers for sentiment classification. The experimental results show that our model improves training data quality and therefore helps MNB boost its performance to achieve the best results for explicit and hidden imbalanced datasets.

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WordNet based Implicit Aspect Sentiment Analysis for Crime Identification from Twitter

Cited by 16 publications

References 18 publications

Crime prediction using a hybrid sentiment analysis approach based on the bidirectional encoder representations from transformers

Crime prediction using a hybrid sentiment analysis approach based on the bidirectional encoder representations from transformers

A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis

An Enhanced MNB Based Model for Explicit and Hidden Sentiment Classification in Imbalanced Datasets

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