Sentiment Analysis Using Deep Learning

Shilpa, Patel; Jacob, Susmi; Vinod, P.

doi:10.1109/icicv50876.2021.9388382

Cited by 35 publications

(9 citation statements)

References 22 publications

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“…Remove English stop words ( Shilpa et al, 2021 ), which are words that are very frequent in a language and are thus considered to not contain much information on the analyzed text such as “the”, “which”, “where”, “and”, “is”, “how”, and “who” from the text using the NLK.corpus and remove any word having less than three characters since such words do not provide much information.…”

Section: Methods For Sentiment Analysis Of Patients’ Drug Reviewsmentioning

confidence: 99%

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Deep learning-based method for sentiment analysis for patients’ drug reviews

Al-Hadhrami,

Vinko,

Al-Hadhrami

et al. 2024

PeerJ Computer Science

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This article explores the application of deep learning techniques for sentiment analysis of patients’ drug reviews. The main focus is to evaluate the effectiveness of bidirectional long-short-term memory (LSTM) and a hybrid model (bidirectional LSTM-CNN) for sentiment classification based on the entire review text, medical conditions, and rating scores. This study also investigates the impact of using GloVe word embeddings on the model’s performance. Two different drug review datasets were used to train and test the models. The proposed methodology involves the implementation and evaluation of both deep learning models with the GloVe word embeddings for sentiment analysis of drug reviews. The experimental results indicate that Model A (Bi-LSTM-CNN) achieved an accuracy of 96% and Model B (Bi-LSTM-CNN) performs consistently at 87% for accuracy. Notably, the incorporation of GloVe word representations improves the overall performance of the models, as supported by Cohen’s Kappa coefficient, indicating a high level of agreement. These findings showed the efficacy of deep learning-based approaches, particularly bidirectional LSTM and bidirectional LSTM-CNN, for sentiment analysis of patients’ drug reviews.

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Section: Methods For Sentiment Analysis Of Patients’ Drug Reviewsmentioning

confidence: 99%

“… Shilpa et al (2021) considered the use of GloVe word embedding with the DBSCAN clustering algorithm in document clustering. The preprocessing is done with and without stemming from Wikipedia and IMDB datasets.…”

Section: Related Workmentioning

confidence: 99%

Deep learning-based method for sentiment analysis for patients’ drug reviews

Al-Hadhrami,

Vinko,

Al-Hadhrami

et al. 2024

PeerJ Computer Science

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“…For instance, IoT gadgets in an intelligent house automatically communicate with one another to create a fully intelligent home (16) . To learn various levels of abstraction in data structures, DL techniques employ a computational framework that incorporates several layers.…”

Section: And ML For Iot Security and Privacymentioning

confidence: 99%

Implementation of Machine Learning and Deep Learning for Securing the Devices in IOT Systems

Jagannath¹,

Mohite²,

Gupta³

et al. 2023

IJST

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Objectives: To fix the security issues of devices in internet of things (IOT) systems that arise when Machine learning (ML) and Deep Learning algorithm (DLA) are implemented in the IOT systems. Methods: Each packet of IoT threats has been filtered by using suitable attack model for primary attack detection. In deep learning, network traffic field's information is extracted from of packet and is used as the training features. Attack is proposed to be detected by the use of database-based features of attack. If attack is found then packet will be discarded along with update feedback to filtering stage for primary attack detection. Knowledge Discovery and Data Mining (KDD) dataset is used in this study. Different types of Mirai attack are considered for classification. The performance of the proposed method has been compared in terms of accuracy and execution time with earlier work using traditional ML-DL methods. Findings: The proposed technique is seen to achieve high accuracy level at the least computational time, concurrently with much higher recall and G-mean values Several algorithms are used to secure the IOT devices from various types of threats and a comparison is depicted with their accuracy and execution time. Results show that the proposed methodology using Convolution neural network (CNN) for classification, can achieve the accuracy ~0.9976 with execution time1.30 sec and G-mean 0.7865 only. Novelty: The proposed method is essentially a judiciously configured ML-DL technique which is novel and exhibits better performance in terms of mitigating IoT threats.

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“…According to (14) categories, expressing emotions into good or negative emotions and employing deep learning approaches in this case. Anger, boredom, emptiness, hatred, sorrow, and concern are negative emotions, whereas excitement, fun, happiness, love, neutrality, and relief are further subdivided into upbeat categories.…”

Section: Introductionmentioning

confidence: 99%

Context Aware Image Sentiment Classification using Deep Learning Techniques

Agarwal¹,

Gupta²

2022

IJST

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Objectives: To propose context aware sentiment classification using deep learning techniques. Methods: We used EfficientNetB-7 deep learning framework for caption generation for the input image and to classify the sentiment of generated caption using machine learning techniques. First, we employ several real-time and synthetic image datasets, then apply pre-processing and normalization for data balancing. Then efficient module implementation for feature extraction and selection using convolutional and pooling layers were done. Despite this proceeding, it generates the caption for respective images. The various feature extraction and selection Natural Language Processing (NLP) techniques such as TF-IDF, lemmas, dependency and correlational features have been used and classify the sentiment label using attention model and greedy approach. Finally, generating the blue score for the entire testing dataset and show the effectiveness of the proposed system. Findings: Our model gives higher accuracy with different deep learning techniques which is demonstrated in result section. The proposed model archives 73.80% average accuracy for EMOTIC dataset. The module has evaluated with different features and deep learning classification algorithms proposed earlier. Novelty: This research is the collaboration of Deep learning and machine learning classification techniques. We first extract the visual features from the input image using deep learning and classify with machine learning with the collaboration of NLP processes. We also carried out various feature extraction techniques such as Ngram, dependency features, co-relational features and determined the sentiment of generated captions.

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Sentiment Analysis Using Deep Learning

Cited by 35 publications

References 22 publications

Deep learning-based method for sentiment analysis for patients’ drug reviews

Deep learning-based method for sentiment analysis for patients’ drug reviews

Implementation of Machine Learning and Deep Learning for Securing the Devices in IOT Systems

Context Aware Image Sentiment Classification using Deep Learning Techniques

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