Sentiment Analysis using Feature Extraction and Dictionary-Based Approaches

Deepa, D.; Raaji,; Tamilarasi, A.

doi:10.1109/i-smac47947.2019.9032456

Cited by 25 publications

(14 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, in addition to semantic feature learning, improving the computational environment with advanced feature selection, resampling, and classification is a must. Moreover, assessing the suitability of the aforesaid methods also needed to be tested with dynamic distributed data with interoperable heterogeneous query elements [51][52][53]. These key factors can be considered the key driving forces behind this study.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Data Infrastructure Security for Interoperable e-Healthcare Systems: A Semantic Feature-Driven NoSQL Intrusion Attack Detection Model

Sreejith

Senthil

2022

BioMed Research International

View full text Add to dashboard Cite

The exponential rise in advanced software computing and low-cost hardware has broadened the horizon for the Internet of Medical Things (IoMT), interoperable e-Healthcare systems serving varied purposes including electronic healthcare records (EHRs) and telemedicine. However, being heterogeneous and dynamic in nature, their database security remains a challenge forever. Numerous intrusion attacks including bot-attack and malware have confined major classical databases towards e-Healthcare. Despite the robustness of NoSQL over the structured query language databases, the dynamic data nature over a heterogeneous environment makes it vulnerable to intrusion attacks, especially over interoperable e-Healthcare systems. Considering these challenges, this work proposed a first of its kind semantic feature-driven NoSQL intrusion attack (NoSQL-IA) detection model for interoperable e-Healthcare systems. This work assessed the efficacy of the different semantic feature-extraction methods like Word2Vec, Continuous Bag of Words, N-Skip Gram (SKG), Count Vectorizer, TF-IDF, and GLOVE towards NoSQL-IA prediction. Subsequently, to minimize computational exhaustion, different feature selection methods including Wilcoxon Rank Sum Test (WRST), significant predictor test, principal component analysis, Select K-Best, and variance threshold feature selection algorithms were employed. To alleviate the data imbalance problem, it applied different resampling methods including upsampling, downsampling, and synthetic minority oversampling technique (SMOTE) over the selected features. Later, Min–Max normalization was performed over the input feature vectors to alleviate any possibility of overfitting. Towards NoSQL-IA prediction, different machine learning methods like Multinomial Naïve Bayes, decision tree, logistic regression, support vector machine, k-NN, AdaBoost, Extra Tree Classifier, random forest ensemble, and XG-Boost were applied, which classified each input query as the regular query or the NoSQL-IA attack query. The depth performance assessment revealed that the use of Word2Vec features SKG in sync with VTFS feature selection and SMOTE resampling processed with the bootstrapped random forest classifier can provide the best performance in terms of high accuracy (98.86%), F-Measure (0.974), and area under the curve (AUC) (0.981), thus enabling it suitable for interoperable e-Healthcare database security.

show abstract

Section: Related Workmentioning

confidence: 99%

Dynamic Data Infrastructure Security for Interoperable e-Healthcare Systems: A Semantic Feature-Driven NoSQL Intrusion Attack Detection Model

Sreejith

Senthil

2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…In topic modelling techniques, both probabilistic and non-probabilistic models were discussed but the LDA technique from probabilistic models was the most commonly used to extract unsupervised topics from a corpus. Research on the fourth question investigated the community's work from other industry applications with short text analysis was discussed to understand [32], [56] and adapt to the education application domain.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, count vectorizer, TF-IDF and Word2Vec techniques were compared using a logistic regression model. Deepa et al [32] proposed an approach to detect the polarity of words from Twitter using three feature extraction techniques count vectorizer, Word2Vec, TF-IDF and two dictionary-based methods of valence aware dictionary and sentiment reasoner (VADER) and SentiWordNet. Feature extraction techniques achieved better accuracy than dictionarybased methods.…”

Section: A Feature Extractionmentioning

confidence: 99%

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

Shaik

Tao

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI's impact on education outlining with current opportunities. In the education domain, student feedback data is crucial to uncover the merits and demerits of existing services provided to students. AI can assist in identifying the areas of improvement in educational infrastructure, learning management systems, teaching practices and study environment. NLP techniques play a vital role in analyzing student feedback in textual format. This research focuses on existing NLP methodologies and applications that could be adapted to educational domain applications like sentiment annotations, entity annotations, text summarization, and topic modelling. Trends and challenges in adopting NLP in education were reviewed and explored. Contextbased challenges in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based sentiment analysis are explained with existing methodologies to overcome them. Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.

show abstract

“…We extract feature vectors with the help of the Bag-of-words method. [28]. Once the data is ready, we build our machine learning model for sentiment analysis and emotion detection.…”

Section: Research Design and Methodologymentioning

confidence: 99%

Stress Detection using Natural Language Processing and Machine Learning over social Interactions

Nijhawan

Attigeri

Ananthakrishna

2021

Preprint

View full text Add to dashboard Cite

Cyberspace is a vast soapbox for people to post anything that they witness in their day-to-day lives. Subsequently, it can be used as a very effective tool in detecting the stress levels of an individual based on the posts and comments shared by him/her on social networking platforms. We leverage large-scale datasets with tweets to successfully accomplish sentiment analysis with the aid of machine learning algorithms. We take the help of a capable deep learning pre-trained model called BERT to solve the problems which come with sentiment classification. The BERT model outperforms a lot of other well-known models for this job without any sophisticated architecture. We also adopted Latent Dirichlet Allocation which is an unsupervised machine learning method that’s skilled in scanning a group of documents, recognizing the word and phrase patterns within them, and gathering word groups and alike expressions that most precisely illustrate a set of documents. This helps us predict which topic is linked to the textual data. With the aid of the models suggested, we will be able to detect the emotion of users online. We are primarily working with Twitter data because Twitter is a website where people express their thoughts often. In conclusion, this proposal is for the well- being of one’s mental health. The results are evaluated using various metric at macro and micro level and indicate that the trained model detects the status of emotions bases on social interactions.

show abstract

Sentiment Analysis using Feature Extraction and Dictionary-Based Approaches

Cited by 25 publications

References 6 publications

Dynamic Data Infrastructure Security for Interoperable e-Healthcare Systems: A Semantic Feature-Driven NoSQL Intrusion Attack Detection Model

Dynamic Data Infrastructure Security for Interoperable e-Healthcare Systems: A Semantic Feature-Driven NoSQL Intrusion Attack Detection Model

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

Stress Detection using Natural Language Processing and Machine Learning over social Interactions

Contact Info

Product

Resources

About