Aspect-based opinion mining framework using heuristic patterns

Asghar, Muhammad Zubair; Khan, Amin; Zahra, Syeda Rabail; Ahmad, Shakeel; Kundi, Fazal Masud

doi:10.1007/s10586-017-1096-9

Cited by 49 publications

(32 citation statements)

References 25 publications

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“…Unsupervised techniques like clustering, have successfully been applied in different domains like aspect-based sentiment analysis [19], stock prediction [20] and sentiment classification. Skillicorn [21], in his work on crime investigations, proposed a framework for the adversarial analysis of data.…”

Section: Related Workmentioning

confidence: 99%

“…Hybrid approach for developing sentiment-based applications have received considerable attention of researchers in different domains, such as business, health-care and politics [23]. In such approaches, different features of supervised, unsupervised and semi supervised techniques are adopted [19]. In the context of extremist affiliation classification, Zeng et al [24] worked on the Chinese text segmentation issue in terrorism domain using a suffix tree and mutual information.…”

Section: Related Workmentioning

confidence: 99%

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Detection and classification of social media-based extremist affiliations using sentiment analysis techniques

Ahmad

Asghar

Alotaibi

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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With the tremendous increase in the use of social network sites like Twitter and Facebook, online community is exchanging information in the form of opinions, sentiments, emotions, and intentions, which reflect their affiliations and aptitude towards an entity, event and policy [1-3]. The propagation of extremist content has also been increasing and being considered as a serious issue in the recent era due to the rise of militant groups such as Irish Republican Army, Revolutionary Armed Forces of Colombia (FARC), Al Quaeda, ISIS (Daesh), Al Shabaab, Taliban, Hezbollah and others [4]. These groups have spread their roots not only at the community levels but also their networks are gaining control of social networking sites [5]. These networking sites are vulnerable and approachable platforms for the group strengthening, propaganda, brainwashing, and fundraising due to its massive impact on public sentiments and opinions. Opinions expressed on such sites give an important clue about the activities and behavior of online users. Detection of such extremist content is important to analyze user sentiment towards some extremist group and to discourage such associated unlawful acts. It is also beneficial in terms of classifying user's extremist affiliation by filtering tweets prior to their onward transmission, recommendation or training AI Chatbot from tweets [6].

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Detection and classification of social media-based extremist affiliations using sentiment analysis techniques

Ahmad

Asghar

Alotaibi

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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“…al. [7] proposed a heuristic pattern based framework for aspect-based opinion mining. Authors advised a framework containing modules for aspect extraction, summary generation and hybrid sentiment classifier dealing with intensifiers and negations.…”

Section: B Existing Methodologiesmentioning

confidence: 99%

“…Then a test set is utilized to verify the model by deriving the class labels of unknown features. Some feature components that are used for feature categorization are unigrams, bigrams NLP based [1], [7] and ontology-based features [13]. Now days, many systems use word dependency-based and ontology-based features [16] to train the classifier.…”

Section: Fig 1: Supervised Learning Approach For Sa: a Basic Model Viewmentioning

confidence: 99%

Aspect Category Extraction for Sentiment Analysis using Multivariate Filter Method of Feature Selection

Bhamare¹,

Jeyanthi²,

Subhashini³

2019

IJRTE

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Aspect-oriented sentiment analysis is done in two phases like aspect term identification from review and determining related opinion. To carry out this analysis, features play an important role to determine the accuracy of the model. Feature extraction and feature selection techniques contribute to increase the classification accuracy. Feature selection strategies reduce computation time, improve prediction performance, and provides a higher understanding of the information in machine learning and pattern recognition applications etc. This work specifically focuses on aspect extraction from restaurant review dataset but can also be used for other datasets. In this system, we proposed a multivariate filter strategy of feature selection which works on lemma features. This method helps to select relevant features and avoid redundant ones. Initially, the extracted features undergo preprocessing and then the “term-frequency matrix” is generated which contains the occurrence count of features with respect to aspect category. In the next phase, different feature selection strategies are applied which includes selecting features based on correlation, weighted term frequency and weighted term frequency with the correlation coefficient. The performance of weighted term frequency with correlation coefficient approach is compared with the existing system and shows significant improvement in F1 score

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“…In this phase, input text from student feedback is classified as subjective or objective using different opinion lexicons. The objective text contains no opinion words, whereas the subjective text includes opinionated terms [26].…”

Section: Subjectivity Detectionmentioning

confidence: 99%

Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

Asghar¹,

Ullah²,

Shamshirband³

et al. 2019

Preprint

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The feedback collection and analysis has remained an important subject matter since long. The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis. However, the student expresses their feedback opinions on online social media sites, which need to be analyzed. This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews. Our technique computes the sentiment score of student feedback reviews and then applies fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level. The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.

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Aspect-based opinion mining framework using heuristic patterns

Cited by 49 publications

References 25 publications

Detection and classification of social media-based extremist affiliations using sentiment analysis techniques

Detection and classification of social media-based extremist affiliations using sentiment analysis techniques

Aspect Category Extraction for Sentiment Analysis using Multivariate Filter Method of Feature Selection

Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

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