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Sentiment analysis using deep learning techniques: a comprehensive review

INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL (2023)

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Article Computer Science, Cybernetics

Combined deep learning classifiers for stock market prediction: integrating stock price and news sentiments

B. L. Shilpa et al.

Summary: This paper introduces a prediction framework via sentiment analysis, considering both stock data and news sentiment data. Technical indicator-based features such as MACD, RSI, and MA are extracted from the stock data. The sentiment of the news data is determined through processes like pre-processing, keyword extraction, and feature extraction. The proposed NN + DBN + SIWOA model shows better performance compared to existing classifiers and optimization algorithms in terms of measures like MAE, MSE, and RMSE. This work contributes to the field of stock market prediction.

KYBERNETES (2023)

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Article Computer Science, Information Systems

A Novel Deep Learning-based Sentiment Analysis Method Enhanced with Emojis in Microblog Social Networks

Xianyong Li et al.

Summary: This paper proposes an emoji-text integrated bidirectional LSTM model for sentiment analysis in microblog social networks. The model extracts review text-based sentence representations using a bidirectional LSTM network and obtains emoji-based auxiliary representations through a new attention mechanism. The two representations are further integrated into final review representation vectors.

ENTERPRISE INFORMATION SYSTEMS (2023)

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Article Computer Science, Information Systems

Business intelligence using deep learning techniques for social media contents

Tarek Kanan et al.

Summary: This study proposes an application that detects satisfaction tone on social media, using machine learning and deep learning techniques. The study simplifies the concept of big data analytics for business on social media by using natural language processing tools. Evaluation of classifiers shows that the Random Forest classifier and Support Vector Machine achieve good results, while the Deep Neural Networks algorithm performs the best.

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS (2023)

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Article Computer Science, Theory & Methods

A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis

Gagandeep Kaur et al.

Journal of Big Data (2023)

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Article Computer Science, Artificial Intelligence

Multi-label emotion classification in texts using transfer learning

Iqra Ameer et al.

Summary: Social media is a valuable platform for understanding people's emotions. The use of multiple attention mechanisms and Transformer networks has been shown to improve accuracy in emotion classification.

EXPERT SYSTEMS WITH APPLICATIONS (2023)

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Article Computer Science, Artificial Intelligence

SARWAS: Deep ensemble learning techniques for sentiment based recommendation system

Chaitali Choudhary et al.

Summary: In this work, an ensemble based deep learning method is used to develop a recommendation model that analyzes both ratings and reviews simultaneously. Sentiment analysis is conducted on reviews to obtain numerical values, and the recommendation system is based on deep learning with varying hidden layers. However, the model can still be improved, particularly in terms of its inability to explain the deep learning recommendation system, which affects its credibility. The work is evaluated based on measures such as precision, loss, and execution time.

EXPERT SYSTEMS WITH APPLICATIONS (2023)

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Article Automation & Control Systems

Improved Feature Representation Using Collaborative Network for Cross-Domain Sentiment Analysis

M. Gunasekar et al.

Information Technology and Control (2023)

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Article Computer Science, Artificial Intelligence

Deep learning based sentiment analysis of public perception of working from home through tweets

Aarushi Vohra et al.

Summary: This study analyzes the trend of working from home using Twitter data. By utilizing a deep learning model and FastText word embeddings, the sentiment of tweets is classified, revealing a majority of positive sentiments towards remote work.

JOURNAL OF INTELLIGENT INFORMATION SYSTEMS (2023)

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Article Computer Science, Information Systems

Impact of convolutional neural network and FastText embedding on text classification

Muhammad Umer et al.

Summary: This study investigates the application of CNN model on text classification problems through experimentation and analysis, and achieves promising results using Fast Text as word embedding.

MULTIMEDIA TOOLS AND APPLICATIONS (2023)

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Article Computer Science, Hardware & Architecture

MAPA BiLSTM-BERT: multi-aspects position aware attention for aspect level sentiment analysis

Mayur Wankhade et al.

Summary: This study proposes a framework called MAPA BiLSTM-BERT model to address unresolved issues in fine-grained sentiment categorization. By introducing explicit multiple aspect position-aware attention and BERT aspect-specific attention, the model achieves significant performance improvement in the evaluation and attention of different aspects.

JOURNAL OF SUPERCOMPUTING (2023)

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Article Business

Customer centric hybrid recommendation system for E-Commerce applications by integrating hybrid sentiment analysis

Arodh Lal Karn et al.

Summary: This paper discusses the importance of recommender systems in internet applications and the role of sentimental analysis in improving recommendation accuracy. By proposing a recommendation model based on a hybrid recommendation model and sentimental analysis, the performance of the recommender system can be enhanced.

ELECTRONIC COMMERCE RESEARCH (2023)

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Integrating BERT Embeddings and BiLSTM for Emotion Analysis of Dialogue

Zhinan Gou et al.

Computational Intelligence and Neuroscience (2023)

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Article Chemistry, Multidisciplinary

RoBERTa-GRU: A Hybrid Deep Learning Model for Enhanced Sentiment Analysis

Kian Long Tan et al.

Summary: This paper proposes a novel hybrid model for sentiment analysis, combining the strengths of the Transformer model (RoBERTa) and the Recurrent Neural Network (GRU). By leveraging the attention mechanism of RoBERTa and the ability of GRU to capture long-range dependencies, the model achieves robust and accurate sentiment classification. The use of data augmentation with word embeddings further enhances the model's representation capacity and addresses imbalanced datasets. Evaluation on three sentiment analysis datasets demonstrates the effectiveness of the proposed RoBERTa-GRU hybrid model, achieving high accuracies.

APPLIED SCIENCES-BASEL (2023)

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Article Computer Science, Artificial Intelligence

Deep Learning-based Integrated Framework for stock price movement prediction

Yanli Zhao et al.

Summary: In this paper, a novel hybrid model SA-DLSTM is proposed for stock market prediction and simulation trading, which combines emotion enhanced convolutional neural network (ECNN), denoising autoencoder (DAE) models, and long short-term memory model (LSTM). User-generated comments on the Internet are used as a complement to stock market data, and ECNN is applied to extract sentiment representation. Key features of stock market data are extracted by DAE to improve prediction accuracy. The timeliness of emotion on the stock market is considered and more reliable and realistic sentiment indexes are constructed. The key features of stock data and sentiment indexes are fed into LSTM for stock market prediction. Experiment results show that SA-DLSTM outperforms other compared models in terms of prediction accuracy and has a good performance in return and risk, assisting investors in making wise decisions.

APPLIED SOFT COMPUTING (2023)

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Article Computer Science, Artificial Intelligence

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

Wenxuan Zhang et al.

Summary: This article introduces the extensive attention given to aspect-based sentiment analysis (ABSA) in the past decade and presents a new taxonomy for ABSA to summarize existing research. The article also summarizes the use of pre-trained language models in ABSA and discusses techniques for building more practical ABSA systems. Finally, the article reviews emerging topics and discusses the future directions of ABSA.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2023)

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Article Green & Sustainable Science & Technology

Hybrid Feature Extraction for Multi-Label Emotion Classification in English Text Messages

Zahra Ahanin et al.

Summary: Emotions are crucial for identifying an individual's attitude and mental state. Detecting and classifying emotions can enhance Human-Computer Interaction systems in Natural Language Processing applications, leading to more effective decision-making in organizations. This paper proposes a hybrid feature extraction model that combines engineered features with deep learning based features for emotion classification in English text.

SUSTAINABILITY (2023)

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Proceedings Paper Computer Science, Artificial Intelligence

Twitter Sentiment Analysis Using Enhanced BERT

Suman Mann et al.

Summary: With the rapid development of social platforms on the Internet, consumer evaluations have become crucial for clients to understand products and make decisions. Sentiment analysis techniques are used to recognize users' emotions and perceptions based on tweets and short messages. This paper explores the application of Enhanced BERT models in sentiment analysis.

INTELLIGENT SYSTEMS AND APPLICATIONS, ICISA 2022 (2023)

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Article Computer Science, Interdisciplinary Applications

Deep-learning-assisted business intelligence model for cryptocurrency forecasting using social media sentiment

Muhammad Yasir et al.

Summary: This research study proposes a business intelligence model to predict cryptocurrency prices using deep learning and SVR, and enhances the model's performance by incorporating social media sentiment data.

JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT (2023)

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Article Computer Science, Artificial Intelligence

Improving aspect-level sentiment analysis with aspect extraction

Navonil Majumder et al.

Summary: Aspect-based sentiment analysis consists of aspect extraction and labelling aspect sentiment polarity. This study shows that transferring knowledge from a pre-trained model can improve the performance of sentiment analysis models and the improvement can be applied across different domains.

NEURAL COMPUTING & APPLICATIONS (2022)

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Article Computer Science, Artificial Intelligence

A survey on sentiment analysis methods, applications, and challenges

Mayur Wankhade et al.

Summary: This article provides an overview of sentiment analysis, discussing its applications, advantages, and challenges. Sentiment analysis involves extracting subjective information from text and evaluating and categorizing people's opinions.

ARTIFICIAL INTELLIGENCE REVIEW (2022)

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Review Computer Science, Interdisciplinary Applications

AMMU: A survey of transformer-based biomedical pretrained language models

Katikapalli Subramanyam Kalyan et al.

Summary: Transformer-based pretrained language models have revolutionized natural language processing in the biomedical research community. This survey comprehensively explores the core concepts, taxonomy, challenges, and possible solutions of these models, providing valuable insights for further advancements.

JOURNAL OF BIOMEDICAL INFORMATICS (2022)

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Review Computer Science, Hardware & Architecture

CBVoSD: context based vectors over sentiment domain ensemble model for review classification

Mayur Wankhade et al.

Summary: With the rapid growth of e-commerce, the number of online product reviews has significantly increased, with historical reviews influencing buyers' decisions despite sentiment expression variations. The neglect of sentiment information in word vectors has led to the proposal of the CBVoSD algorithm to improve sentiment classification accuracy.

JOURNAL OF SUPERCOMPUTING (2022)

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Article Computer Science, Artificial Intelligence

Contextual sentiment embeddings via bi-directional GRU language model

Jin Wang et al.

Summary: Sentiment embeddings can distinguish words with similar contexts but opposite sentiment, yet traditional methods may ignore the variation of word meaning in different contexts. Dealing with out-of-vocabulary or informal-writing sentiment words is a challenge. The proposed training model effectively addresses these issues and outperforms previous methods.

KNOWLEDGE-BASED SYSTEMS (2022)

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Article Computer Science, Artificial Intelligence

Aspect-level sentiment analysis with aspect-specific context position information

Bo Huang et al.

Summary: In recent years, researchers have paid more attention to aspect-level sentiment analysis in natural language processing. A fine-grained sentiment analysis distinguishes each aspect of the text and makes separate judgments on the sentiment polarity. This paper proposes an aspect-level sentiment analysis model with aspect-specific contextual location information, adjusting the weight of contextual words and extracting the influence of contextual association on individual sentence sentiment polarity.

KNOWLEDGE-BASED SYSTEMS (2022)

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Article Chemistry, Multidisciplinary

Transformer-Based Graph Convolutional Network for Sentiment Analysis

Barakat AlBadani et al.

Summary: Sentiment Analysis is an important research topic in the field of natural language processing, and this paper proposes a new Transformer-based graph convolutional network model called Sentiment Transformer Graph Convolutional Network (ST-GCN). The model successfully models the sentiment corpus as a heterogeneous graph and learns document and word embeddings using the proposed sentiment graph transformer neural network. Experimental results show that the model outperforms existing state-of-the-art models on four standard datasets.

APPLIED SCIENCES-BASEL (2022)

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Article Computer Science, Information Systems

RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network

Kian Long Tan et al.

Summary: Due to the rapid development of technology, social media has become increasingly common in people's daily lives. Sentiment analysis is an important technique to understand the sentiment behind texts. However, sentiment analysis faces challenges such as lexical diversity, imbalanced datasets, and long-distance dependencies. In this paper, a hybrid deep learning method is proposed that combines the strengths of sequence models and Transformer models. Experimental results show that this method outperforms state-of-the-art methods on different datasets.

IEEE ACCESS (2022)

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Article Computer Science, Information Systems

SpotSpam: Intention Analysis-driven SMS Spam Detection Using BERT Embeddings

C. Oswald et al.

Summary: Short Message Service (SMS) is a widely used mobile application for global communication. Filtering spam SMS is a crucial and challenging task, and the existing methods have limitations in focusing only on the textual features of the SMS. This article proposes an intention-based approach for SMS spam filtering, which efficiently handles dynamic keywords by focusing on the semantics of the words.

ACM TRANSACTIONS ON THE WEB (2022)

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Article Computer Science, Artificial Intelligence

S_I_LSTM: stock price prediction based on multiple data sources and sentiment analysis

Shengting Wu et al.

Summary: This paper proposes a stock price prediction method S_I_LSTM that combines multiple data sources and investor sentiment using sentiment analysis and convolutional neural network, as well as long short-term memory network for predicting the China Shanghai A-share market. Experimental results show that the method outperforms traditional methods on real data sets of five listed companies.

CONNECTION SCIENCE (2022)

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Review Computer Science, Interdisciplinary Applications

Medical deep learning-A systematic meta-review

Jan Egger et al.

Summary: Deep learning has made remarkable impact in various scientific disciplines, such as image processing and autonomous driving. It has also shown great potential in the medical domain. However, obtaining a complete overview of the field of 'medical deep learning' is becoming increasingly difficult due to the abundance of patient data and the rapid growth of deep learning research.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2022)

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Article Computer Science, Hardware & Architecture

Efficient spam and phishing emails filtering based on deep learning

Safaa Magdy et al.

Computer Networks (2022)

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Article Computer Science, Artificial Intelligence

A network-based feature extraction model for imbalanced text data

Keping Li et al.

Summary: This paper introduces a network-based Convolutional Neural Network (NCNN) to address the issue of imbalanced data. The proposed model mitigates the effect of imbalanced data by generating new synthetic samples and improves performance by introducing an additional layer. The effectiveness of the proposed NCNN model on imbalanced text data is demonstrated through experiments.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

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Article Computer Science, Artificial Intelligence

A hybrid E-learning recommendation integrating adaptive profiling and sentiment analysis

Hadi Ezaldeen et al.

Summary: This research introduces a novel framework called Enhanced e-Learning Hybrid Recommender System (ELHRS), which provides personalized e-content recommendations based on learners' needs. It develops a new model to automatically infer the Semantic Learner Profile and incorporates sentiment analysis models for rating prediction. The research also explores term expansion using semantic-based approaches and evaluates various language models for better accuracy.

JOURNAL OF WEB SEMANTICS (2022)

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Article Mathematical & Computational Biology

Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques

Anisha P. Rodrigues et al.

Summary: The proposed work aims to develop a system that can determine whether a tweet is spam or ham and evaluate its emotion. Through sentiment analysis, the problem of spam accounts on social media sites can be effectively addressed.

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2022)

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Article Engineering, Multidisciplinary

Business boosting through sentiment analysis using Artificial Intelligence approach

Alim Al Ayub Ahmed et al.

Summary: This paper introduces the application of artificial intelligence in the business sector, with a focus on enhancing business performance through sentiment analysis.

INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT (2022)

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Set-CNN: A text convolutional neural network based on semantic extension for short text classification

Yajian Zhou et al.

Summary: In this paper, a semantic extension-based classification algorithm for short texts called Set-CNN is proposed. Set-CNN enriches the features of short texts using a semantic extension mechanism and captures semantic features at different levels through a multiple-channel convolutional framework. Experimental results show that Set-CNN outperforms state-of-the-art alternatives and exhibits excellent performance as a lightweight text classifier.

KNOWLEDGE-BASED SYSTEMS (2022)

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Article Computer Science, Artificial Intelligence

MobyDeep: A lightweight CNN architecture to configure models for text classification

R. Romero et al.

Summary: Currently, deep learning trends in text classification focus on creating complex models for handling large datasets. This paper introduces a new Convolutional Neural Network (CNN) architecture (MobyDeep) for text classification tasks, designed as a lightweight model capable of managing large corpora under low computational costs. The architecture incorporates ideas from image to text processing, resulting in a new convolutional block that helps reduce model sizes and computational costs. Furthermore, the architecture utilizes residual connections and extends the models up to 28 layers, achieving competitive accuracy results when compared with other state-of-the-art works in real scenarios.

KNOWLEDGE-BASED SYSTEMS (2022)

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Article Computer Science, Hardware & Architecture

KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis

Dimple Tiwari et al.

Summary: Social media serves as a platform for sharing opinions, sentiment analysis applications address challenges in handling massive reviews, a new model is proposed to enhance sentiment analysis, emphasizing the role of artificial intelligence in managing global pandemics and local disputes.

NEW GENERATION COMPUTING (2022)

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Article Computer Science, Information Systems

Document-Level Sentiment Analysis Using Attention-Based Bi-Directional Long Short-Term Memory Network and Two-Dimensional Convolutional Neural Network

Yanying Mao et al.

Summary: This paper proposes a new neural network model, AttBiLSTM-2DCNN, for sentiment analysis on long texts and addresses the challenge of differentiating the importance of document features. The experimental results demonstrate that the model can capture sentimental relations and outperform certain state-of-the-art models.

ELECTRONICS (2022)

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Article Computer Science, Artificial Intelligence

Explicit and implicit oriented Aspect-Based Sentiment Analysis with optimal feature selection and deep learning for demonetization in India

K. Ananthajothi et al.

Summary: Aspect-Based Sentiment Analysis (ABSA) is a popular scheme that predicts the sentiment of positive characteristics in text using deep neural networks. This study focuses on developing a novel ABSA method that considers both explicit and implicit aspects using demonetization dataset reviews from India. The results show that this method provides enriched performance with diverse performance metrics.

DATA & KNOWLEDGE ENGINEERING (2022)

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Article Multidisciplinary Sciences

Opinion analysis and aspect understanding during covid-19 pandemic using BERT-Bi-LSTM ensemble method

Mayur Wankhade et al.

Summary: Social media platforms play a vital role in increasing public awareness about disease severity and preventive measures. This article proposes a novel approach using BERT and Bi-LSTM models for sentiment analysis of coronavirus-related tweets. The proposed method outperforms existing techniques and effectively captures public opinion, which can assist in formulating public health policies.

SCIENTIFIC REPORTS (2022)

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Article Computer Science, Hardware & Architecture

Leveraging Readability and Sentiment in Spam Review Filtering Using Transformer Models

Sujithra Kanmani et al.

COMPUTER SYSTEMS SCIENCE AND ENGINEERING (2022)

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Proceedings Paper Materials Science, Multidisciplinary

A survey on deep learning based sentiment analysis

Jyothis Joseph et al.

Summary: This survey focuses on sentiment analysis using various deep learning methodologies, and finds that recurrent neural networks perform better in this field. Deep learning networks, which can handle large amounts of data, are more suitable for sentiment analysis than support vector machines and conventional neural networks.

MATERIALS TODAY-PROCEEDINGS (2022)

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Proceedings Paper Engineering, Industrial

LSTM-CNN Deep Learning Model for French Online Product Reviews Classification

Nassera Habbat et al.

Summary: Sentiment analysis is crucial in helping enterprises improve business strategies and understand customer feedback. This study used a dataset of French reviews and applied preprocessing, feature extraction, and deep learning algorithms to classify the reviews as positive or negative. Results showed that the combined model (LSTM+CNN) using CamemBERT achieved the highest accuracy of 93.7%.

ADVANCED TECHNOLOGIES FOR HUMANITY (2022)

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Review Computer Science, Information Systems

A review on sentiment analysis and emotion detection from text

Pansy Nandwani et al.

Summary: Social networking platforms are essential for communicating feelings in the Internet era, with sentiment analysis helping to understand human psychology and emotion detection providing more precise insights into individual emotional/mental states.

SOCIAL NETWORK ANALYSIS AND MINING (2021)

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Article Computer Science, Artificial Intelligence

Learning interaction dynamics with an interactive LSTM for conversational sentiment analysis

Yazhou Zhang et al.

Summary: Conversational sentiment analysis is an emerging and challenging task that aims to discover the affective state and sentimental change of each person in a conversation. Current sentiment analysis approaches are inadequate for this subtask due to the lack of benchmark datasets and the difficulty in modeling interactions between individuals.

NEURAL NETWORKS (2021)

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Article Computer Science, Information Systems

Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data

Ranjan Kumar Behera et al.

Summary: The study proposes a hybrid approach using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) for sentiment classification of consumer reviews posted in social media. The experimental results show that the proposed ensemble model outperforms other machine learning approaches in terms of accuracy and other parameters.

INFORMATION PROCESSING & MANAGEMENT (2021)

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Article Engineering, Electrical & Electronic

A Comprehensive Survey on Transfer Learning

Fuzhen Zhuang et al.

Summary: Transfer learning aims to improve the performance of target learners by transferring knowledge from related source domains, reducing the reliance on target-domain data. This survey aims to systematize and summarize existing research studies in order to help readers understand the current status and ideas in the area of transfer learning.

PROCEEDINGS OF THE IEEE (2021)

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Review Computer Science, Artificial Intelligence

Transformer models for text-based emotion detection: a review of BERT-based approaches

Francisca Adoma Acheampong et al.

Summary: The importance of contextual information in NLP applications cannot be emphasized enough, with significant improvements observed in tasks like emotion recognition from texts. This paper discusses transformer-based models for NLP tasks, highlighting the pros and cons of models such as GPT, Transformer-XL, XLM, and BERT. Researchers have proposed various BERT-based models for text-based emotion detection due to BERT's strength and popularity in this field.

ARTIFICIAL INTELLIGENCE REVIEW (2021)

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Article Computer Science, Theory & Methods

ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis

Mohammad Ehsan Basiri et al.

Summary: ABCDM is an attention-based bidirectional CNN-RNN deep model that utilizes bidirectional LSTM and GRU to extract contextual information from both past and future, applying attention mechanism to emphasize different words for sentiment analysis polarity detection.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2021)

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Article Computer Science, Artificial Intelligence

A novel context-aware multimodal framework for persian sentiment analysis

Kia Dashtipour et al.

Summary: Recent studies show that utilizing multimodal data can effectively gauge public perception, and a Persian multimodal dataset and sentiment analysis framework are provided as research resources. Experimental results demonstrate that integrating multimodal features can enhance sentiment analysis performance.

NEUROCOMPUTING (2021)

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Article Computer Science, Software Engineering

Deep Learning Based Techniques for Sentiment Analysis: A Survey

Wael Etaiwi et al.

Summary: Natural Language Processing (NLP) involves the automated representation of human language using various techniques, with sentiment classification being a fundamental activity in sentiment analysis. Over the past five years, there has been a focus on deep learning methods in sentiment analysis.

INFORMATICA-AN INTERNATIONAL JOURNAL OF COMPUTING AND INFORMATICS (2021)

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Article Computer Science, Artificial Intelligence

A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction

Nan Jing et al.

Summary: This paper proposes a hybrid model combining deep learning with sentiment analysis for stock price prediction. Real-life experiments conducted validate the model's superior performance in classifying investor sentiments and predicting stock prices.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

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Article Computer Science, Information Systems

A creative approach to understanding the hidden information within the business data using Deep Learning

Yuanfeng Luo et al.

Summary: The proposed DNNITS model utilizes deep neural networks for efficient and secure business data hiding in regular files, outperforming existing models in extraction rate, significance rate, performance ratio, efficiency ratio, recall ratio, and error rate.

INFORMATION PROCESSING & MANAGEMENT (2021)

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Article Computer Science, Artificial Intelligence

A comprehensive survey on sentiment analysis: Approaches, challenges and trends

Marouane Birjali et al.

Summary: Sentiment analysis, also known as Opinion Mining, is the task of extracting and analyzing people's opinions and emotions towards different entities. It is a powerful tool used by businesses, governments, and researchers to gain insights and make better decisions. This paper provides a comprehensive study of sentiment analysis methods, challenges, and trends for researchers in the field.

KNOWLEDGE-BASED SYSTEMS (2021)

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Review Computer Science, Artificial Intelligence

Multimodal sentimental analysis for social media applications: A comprehensive review

Ganesh Chandrasekaran et al.

Summary: Sentiment analysis is crucial for identifying and classifying opinions on products or services, with traditional text-based methods no longer meeting the needs of analyzing multimodal data effectively.

WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Effect of Negation in Sentences on Sentiment Analysis and Polarity Detection

Partha Mukherjee et al.

Summary: Sentiment analysis is a sub-field of NLP that helps companies understand customer opinions in the global marketplace. Negative sentences and negations have a significant impact on sentiment polarity detection, and processing them incorrectly can lead to biases and misclassification. This paper introduces a novel sentiment analysis approach that includes a customized negation marking algorithm, achieving improved accuracy in experiments with machine learning algorithms on Amazon reviews.

BIG DATA, IOT, AND AI FOR A SMARTER FUTURE (2021)

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Article Computer Science, Information Systems

ALBERTC-CNN Based Aspect Level Sentiment Analysis

Xingxin Ye et al.

Summary: This study introduces a new aspect-level sentiment analysis model named ALBERTC-CNN, which combines different network advantages to improve emotion classification accuracy. The model is tested on two datasets and achieves promising results compared with traditional networks.

IEEE ACCESS (2021)

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Article Computer Science, Artificial Intelligence

Stock closing price prediction based on sentiment analysis and LSTM

Zhigang Jin et al.

NEURAL COMPUTING & APPLICATIONS (2020)

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Sentiment analysis using deep learning architectures: a review

Ashima Yadav et al.

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Semantic relation extraction using sequential and tree-structured LSTM with attention

ZhiQiang Geng et al.

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A deep learning architecture of RA-DLNet for visual sentiment analysis

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Rohit Kumar Kaliyar et al.

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A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques

Mohammad Ehsan Basiri et al.

KNOWLEDGE-BASED SYSTEMS (2020)

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Article Computer Science, Artificial Intelligence

A T1OWA and aspect-based model for customizing recommendations on eCommerce

Jesus Serrano-Guerrero et al.

APPLIED SOFT COMPUTING (2020)

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Proceedings Paper Computer Science, Artificial Intelligence

Visualizing Transformers for NLP: A Brief Survey

Adrian M. P. Brasoveanu et al.

2020 24TH INTERNATIONAL CONFERENCE INFORMATION VISUALISATION (IV 2020) (2020)

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Article Computer Science, Cybernetics

Aspect-Based Sentiment Analysis: A Survey of Deep Learning Methods

Haoyue Liu et al.

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2020)

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Article Computer Science, Theory & Methods

Toward multi-label sentiment analysis: a transfer learning based approach

Jie Tao et al.

JOURNAL OF BIG DATA (2020)

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Article Computer Science, Information Systems

Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers

Kostadin Mishev et al.

IEEE ACCESS (2020)

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Article Computer Science, Information Systems

Enhancing BERT Representation With Context-Aware Embedding for Aspect-Based Sentiment Analysis

Xinlong Li et al.

IEEE ACCESS (2020)

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Article Computer Science, Artificial Intelligence

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