Sentiment Analysis Domain Research Articles

Sentiment Analysis on Placement Aspect

Sentiment analysis has emerged as a pivotal tool within the realm of natural language processing, finding widespread application across diverse domains such as marketing, social media analysis, and customer feedback assessment. This research delves into the specialized domain of sentiment analysis focused on the placement aspect, which revolves around the intricate process of aligning individuals with suitable job opportunities. The primary objective of this study is to dissect sentiments embedded within textual data associated with placements, including resumes, job listings, and interview appraisals, with the aim of extracting nuanced insights to inform strategic decision-making within recruitment and talent acquisition processes. Leveraging cutting-edge techniques in machine learning and natural language processing, our research employs a multifaceted approach encompassing text preprocessing, feature extraction, and sentiment classification methodologies. A spectrum of sentiment analysis algorithms is explored, ranging from lexicon-based methods to sophisticated machine learning models (such as Support Vector Machines and Naive Bayes) and deep learning architectures (including Recurrent Neural Networks and Transformers). Additionally, our investigation extends to assessing the influence of various factors—such as dataset dimensions, domain specificity, and feature selection techniques—on the accuracy and robustness of sentiment analysis outcomes.

Sentiment analysis in multilingual context: Comparative analysis of machine learning and hybrid deep learning models

This research paper investigates the efficacy of various machine learning models, including deep learning and hybrid models, for text classification in the English and Bangla languages. The study focuses on sentiment analysis of comments from a popular Bengali e-commerce site, "DARAZ," which comprises both Bangla and translated English reviews. The primary objective of this study is to conduct a comparative analysis of various models, evaluating their efficacy in the domain of sentiment analysis. The research methodology includes implementing seven machine learning models and deep learning models, such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional 1D (Conv1D), and a combined Conv1D-LSTM. Preprocessing techniques are applied to a modified text set to enhance model accuracy. The major conclusion of the study is that Support Vector Machine (SVM) models exhibit superior performance compared to other models, achieving an accuracy of 82.56% for English text sentiment analysis and 86.43% for Bangla text sentiment analysis using the porter stemming algorithm. Additionally, the Bi-LSTM Based Model demonstrates the best performance among the deep learning models, achieving an accuracy of 78.10% for English text and 83.72% for Bangla text using porter stemming. This study signifies significant progress in natural language processing research, particularly for Bangla, by enhancing improved text classification models and methodologies. The results of this research make a significant contribution to the field of sentiment analysis and offer valuable insights for future research and practical applications.

Pelican Optimization Algorithm with Deep Learning for Aspect based Sentiment Analysis on Asian Low Resource Languages

Recently, sentiment analysis (SA) has become more popular as it is crucial to moderate and examine the data from the internet. It contains several applications, such as social media monitoring, market research, and opinion mining. Aspect Based Sentiment Analysis (ABSA) is a domain of SA that manages sentiment at a better level. ABSA classifies sentiment in terms of all the aspects for obtaining superior insights as sentiment expressed. A major contribution has been developed in ABSA, and then this progress can be restricted only to some languages with suitable resources. One common method is to utilize machine learning (ML) approaches, namely Neural Networks (NN), Support Vector Machines (SVM), and Naive Bayes (NB), together with Asian and low language-specific resources. These resources offer data on the sentiment polarity (neutral, positive, or negative) of phrases and words that are generally utilized in low-resource languages. In this aspect, this study develops a new Pelican Optimization Algorithm with Deep Learning for ABSA (POADL-ABSA) on Asian and Low Resource Languages. The proposed POADL-ABSA technique focuses on the detection and classification of sentiments. To accomplish this, the POADL-ABSA technique encompasses various levels of operations such as pre-processed, feature vector conversion, and classification. In addition, the POADL-ABSA technique employs the BERT model for feature vector extraction. Besides, attention-based bi-directional long short-term memory (ABiLSTM) system was used for the recognition and classification of sentiments. Finally, the POA was utilized for optimum hyperparameter selection of the ABiLSTM model, and it helps in attaining enhanced sentiment classification results. To ensure the improvised performance of the IAOADL-ABSA technique, an extensive experimental outcome the IAOADL-ABSA technique surpassed other models with acc{u}_y , pre{c}_n , rec{a}_l , and {F}_{score} of 98.72%, 98.71%, 98.72%, and 98.71%, respectively. Therefore, the IAOADL-ABSA technique can be employed for accurate classification results.

Evaluating a New Auto-ML Approach for Sentiment Analysis and Intent Recognition Tasks

Automated Machine Learning (AutoML) is a research area that aims to help humans solve Machine Learning (ML) problems by automatically discovering good ML pipelines (algorithms and their hyperparameters for every stage of a machine learning process) for a given dataset. Since we have a combinatorial optimization problem for which it is impossible to evaluate all possible pipelines, most AutoML systems use a Genetic Algorithm (GA) or Bayesian Optimization (BO) to find a good solution. These systems usually evaluate the performance of the pipelines using the K-fold cross-validation method, for which the more pipelines are evaluated, the higher the chance of finding an overfitted solution. To avoid the aforementioned issue, we propose a system named Auto-ML System for Text Classification (ASTeC), that uses the Bootstrap Bias Corrected CV (BBC-CV) method to evaluate the performance of the pipelines. More specifically, the proposed system combines GA, BO, and BBC-CV to find a good ML pipeline for the text classification task. We evaluated our approach by comparing it with state-of-the-art systems: in the the Sentiment Analysis (SA) task, we compared our approach to TPOT (Tree-based Pipeline Optimization Tool) and Google Cloud AutoML service, and for the Intent Recognition (IR) task, we compared with TPOT and MLJAR AutoML. Concerning the data, we analysed seven public datasets from the SA domain and sixteen from the IR domain. Four out of those sixteen are composed by written English text, while all of the others are in Brazilian Portuguese. Statistical tests show that, in 21 out of 23 datasets, our system's performance is equivalent to or better than the others.

A Comprehensive Roadmap on Bangla Text-based Sentiment Analysis

The effortless expansion of Internet access has eventually transformed the dissemination behavior toward E-Mode. Thus, the usage of online or, more specifically, “Digital” texts has expanded abruptly. “Bangla,” the seventh most spoken language globally, has no different nature. Communication in the Bangla language has also been exposed on the Internet, which describes the feelings of individuals in any specific context. These enormously generated data from diverse sources have drawn the interest of the researchers working in the Natural Language Processing domain. Despite its relatively complicated structure, a lesser amount of annotated data, as well as a limited number of frameworks and approaches, exist. This lacking of resources has kept several stones unturned in this diverse, emotion-rich, and widely spoken language. To bridge the lacking and absence of resources, this article aims to provide a generalized deduced working procedure in this domain. To do so, the existing research work in the domain of sentiment analysis using Bangla text has been collected, evaluated, and summarized. Also, in this article, the techniques used in pre-processing, feature extraction, and eventually used algorithms have been identified and discussed. Considering these facts, this research work sketches a tentative blueprint of sentiment analysis using Bangla text. Additionally, this article discusses existing regional language corpora such as Tamil, Urdu, and Hindi, as well as English and methodologies used to extract emotional essence from Bangla language comparing other languages. That will assist in determining the probable chosen path of exploring Bangla in a deeper aspect. Moreover, this work has deduced and presented a generalized framework that will direct aspiring researchers to decide the pathway of choosing data vis-à-vis methodologies based on their interests.

Developing an Emotion Recognition Tool for Tweets

This research in the domain of sentiment analysis aims to maintain high effectivity and applicability by not just understanding how the user feels, but by accurately quantifying their emotions. With the desired end product being a web application, the visualization that is received as an output aids the user in comparing the intensity of their emotions of anger, fear, joy and sadness demonstrated in the input tweets. This web application was built with the use of a dataset from the SEMEVAL-2018 competition. Training and testing of the dataset using 6 machine learning algorithms and their evaluation using performance metrics including R2, MAE, MSE and RMSE led us to arrive at the result that 'Support Vector Regressor' was the best performing algorithm for anger, sadness and fear while the Gradient Boosting algorithm performed best for joy. To that end, the web application uses the following algorithm for evaluating the respective emotions. In addition to developing a prediction model, the research also involved extensive data visualization an analysis that conveyed the most used words and hashtags when the user experiences each of the aforementioned emotions. To highlight the research's accomplishment- the author has been able to create a fairly accurate and relatively quick sentiment analysis model for public opinion.

Modeling of Optimal Fully Connected Deep Neural Network based Sentiment Analysis on Social Networking Data

Abstract In recent times, the user reviews and opinions postedon social-media like Twitter, Facebook, and Google+, and so on have received considerable attention in the domain of sentiment analysis (SA), offering valued feedback to public as well as private organizations. The examination of reviews roles an essential role for enhancing the product and service qualities. Although numerous SA methods are available in the literature, it is still needed to boost the outcome of the SA for understanding the customer feedbacks and thereby enhances the product quality. The presented article proposesan optimum fully connected deep neural network (OFCDNN) based SA, called OFCDNN-SA technique on social networking data. The OFCDNN-SA technique encompasses distinct procedureslike feature extraction, classification, preprocessing, and parameter optimization. Additionally, Glove technique is applied for the transformation of input data into feature vectors. Moreover, salp swarm optimization (SSO) based hyperparameter optimization technique is derived for maximally selecting the hyperparameters utilized in the DL method. Finally, the FCDNN methodis employed for classification purposes. To investigate the supreme achievement of the OFCDNN-SA method, a wide-ranging simulation assessment is performed and the experimental outputs highlighted the betterment over current methods by means of diverse measures.

Deep Sentiments Analysis for Roman Urdu Dataset Using Faster Recurrent Convolutional Neural Network Model

ABSTRACT Urdu language is being spoken by over 64 million people and its Roman script is very popular, especially on social networking sites. Most users prefer Roman Urdu over English grammar for communication on social networking platforms such as Facebook, Twitter, Instagram and WhatsApp. For research, Urdu is a poor resource language as there are a few research papers and projects that have been carried out for the language and vocabulary enhancement in comparison to other languages especially English. A lot of research has been made in the domain of sentiment analysis in English but only a limited work has been performed on the Roman Urdu language. Sentiment analysis is the method of understanding human emotions or points of view, expressed in a textual form about a particular thing. This article proposes a deep learning model to perform data mining on emotions and attitudes of people using Roman Urdu. The main objective of the research is to evaluate sentiment analysis on Roman Urdu corpus containing RUSA-19 using faster recurrent convolutional neural network (FRCNN), RCNN, rule-based and N-gram model. For assessment, two series of experiments were performed on each model, binary classification (positive and negative) and tertiary classification (positive, negative, and neutral). Finally, the evaluation of the faster RCNN model is analyzed and a comparative analysis is performed for the outcomes of four models. The faster RCNN model outperformed others as the model achieves an accuracy of 91.73% for binary classification and 89.94% for tertiary classification.

Enhanced Arabic Sentiment Analysis Using a Novel Stacking Ensemble of Hybrid and Deep Learning Models

Sentiment analysis (SA) is a machine learning application that drives people’s opinions from text using natural language processing (NLP) techniques. Implementing Arabic SA is challenging for many reasons, including equivocation, numerous dialects, lack of resources, morphological diversity, lack of contextual information, and hiding of sentiment terms in the implicit text. Deep learning models such as convolutional neural networks (CNN) and long short-term memory (LSTM) have significantly improved in the Arabic SA domain. Hybrid models based on CNN combined with long short-term memory (LSTM) or gated recurrent unit (GRU) have further improved the performance of single DL models. In addition, the ensemble of deep learning models, especially stacking ensembles, is expected to increase the robustness and accuracy of the previous DL models. In this paper, we proposed a stacking ensemble model that combined the prediction power of CNN and hybrid deep learning models to predict Arabic sentiment accurately. The stacking ensemble algorithm has two main phases. Three DL models were optimized in the first phase, including deep CNN, hybrid CNN-LSTM, and hybrid CNN-GRU. In the second phase, these three separate pre-trained models’ outputs were integrated with a support vector machine (SVM) meta-learner. To extract features for DL models, the continuous bag of words (CBOW) and the skip-gram models with 300 dimensions of the word embedding were used. Arabic health services datasets (Main-AHS and Sub-AHS) and the Arabic sentiment tweets dataset were used to train and test the models (ASTD). A number of well-known deep learning models, including DeepCNN, hybrid CNN-LSTM, hybrid CNN-GRU, and conventional ML algorithms, have been used to compare the performance of the proposed ensemble model. We discovered that the proposed deep stacking model achieved the best performance compared to the previous models. Based on the CBOW word embedding, the proposed model achieved the highest accuracy of 92.12%, 95.81%, and 81.4% for Main-AHS, Sub-AHS, and ASTD datasets, respectively.

Improving Sentiment Classification Performance through Coaching Architectures.

Intelligent systems have been developed for years to solve specific tasks automatically. An important issue emerges when the information used by these systems exhibits a dynamic nature and evolves. This fact adds a level of complexity that makes these systems prone to a noticeable worsening of their performance. Thus, their capabilities have to be upgraded to address these new requirements. Furthermore, this problem is even more challenging when the information comes from human individuals and their interactions through language. This issue happens more easily and forcefully in the specific domain of Sentiment Analysis, where feelings and opinions of humans are in constant evolution. In this context, systems are trained with an enormous corpus of textual content, or they include an extensive set of words and their related sentiment values. These solutions are usually static and generic, making their manual upgrading almost unworkable. In this paper, an automatic and interactive coaching architecture is proposed. It includes a ML framework and a dictionary-based system both trained for a specific domain. These systems converse about the outcomes obtained during their respective learning stages by simulating human interactive coaching sessions. This leads to an Active Learning process where the dictionary-based system acquires new information and improves its performance. More than 800, 000 tweets have been gathered and processed for experiments. Outstanding results were obtained when the proposed architecture was used. Also, the lexicon was updated with the prior and new words related to the corpus used which is important to reach a better sentiment analysis classification.

Multiclass Intent Analysis: Beyond the Conventional Polarities

The sentiment polarity for an element determines the orientation of the conveyed feeling, namely, whether the text communicates the user's positive, negative, or neutral sentiment toward the object in question. Sentiment analysis is a crucial task in natural language processing, with numerous real-world applications. Most sentiment analysis attempts to predict whether a given text is positive or negative. One of the most efficient and robust methods applied in the domain of sentiment analysis is deep learning. The recent advancements in social networking sites and their growing impact on the behavior of individuals have led to many pieces of research in the field of study of such networking platforms. Multiclass sentiment analysis identifies the exact sentiment delivered by the tweets instead of merely generalizing those sentiments as positive, negative, and neutral. In the case of online buying, most clients resort to the reviews about the same product they want to buy that are posted on the website. This work incorporates understanding the real-time situations of different customers' reviews about several products purchased through online shopping platforms thereafter determining whether their review is an Appreciation, Complaint, Enquiry, or Non-Intent. This paper includes Max-Vote Ensemble Technique, comprising three deep learning models: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a combined model of CNN and LSTM. These models predict the review’s sentiment and strive to classify multiple polarities breaking through the three conventional polarity categories.

SE4SA: a deep syntactical contextualized text representation learning approach for sentiment analysis

Recently, many pre-trained text embedding models have been applied to effectively extract latent features from texts and achieve remarkable performance in various downstream tasks of sentiment analysis domain. However, these pre-trained text embedding models also encounter limitations related to the capability preserving the syntactical structure as well as the global long-range dependent relationships of words. Thus, they might fail to recognize the relevant syntactical features of words as valuable evidences for analyzing sentiment aspects. To overcome these limitations, we proposed a novel deep semantic contextual embedding technique for sentiment analysis, called as: SE4SA. Our proposed SE4SA is a multi-level text embedding model which enables to jointly exploit the long-range syntactical and sequential representations of texts. Then, these achieved rich semantic textual representations can support to have a better understanding on the sentiment aspects of the given text corpus, thereby resulting the better performance on sentiment analysis task. Extensive experiments in several benchmark datasets demonstrate the effectiveness or our proposed SE4SA model in comparing with recent state-of-the-art model.

Joint evaluation of preprocessing tasks with classifiers for sentiment analysis in Brazilian Portuguese language

Sentiment analysis is a task that deals with the automatic extraction of sentimental contents expressed in written text. Several approaches in sentiment analysis are based on machine learning techniques, more specifically classifiers that are trained on labeled datasets. In this context, many Natural Language Processing (NLP) tasks are usually employed as a preprocessing step to help improve the quality of the data and to convert them into forms appropriate for the subsequent classification process. Several studies on sentiment analysis in the literature have already performed some evaluation of NLP tasks and/or classification. However, the vast majority of them did not work with texts in the Brazilian Portuguese language and the analyzes did not consider the combination of sets of preprocessing tasks with classifiers. Therefore, in this work, we evaluate the combination of five NLP tasks and three classifiers in the domain of sentiment analysis using texts written in Portuguese. The experimental results showed that different combinations of preprocessing tasks can significantly affect the predictive performance of a classifier for a given dataset. Thus, it is clear the importance of performing the joint evaluation of preprocessing tasks with classifiers when choosing which preprocessing tasks and classifiers should be used for a dataset.

Sentiment Analysis on Movie Reviews Using Twitter

Machine learning can help people to perform complex tasks and solve problems as it uses historical data to learn its pattern and make predictions based on the past data. This research addresses the problem about movie reviews on social media specifically Twitter; where it will gather the tweets on movie reviews and display a rating based on the sentiment of the tweet. Twitter is an online social media website where people from all walks of life communicate by tweeting short updates without exceeding the character limit which is 240 characters. Twitter is continuously growing as a business and became one of the biggest platform for communication and instant messaging. Due to the large number of users, there are voluminous amounts of data available that can be used for more in depth information and insights and to get the sentiments from analysing the tweets. In today’s world, there are many applications that are using sentiment analysis in various fields such as to gets insights about a particular brand or product. To do sentiment analysis using the traditional ways can be time consuming and becomes very complex. The aim of this research is to investigate about the domain of sentiment analysis and incorporate a machine learning algorithm to create a system that is able to get and display the ratings of a particular movie. The machine learning algorithms used are Naïve Bayes Classifier and SVM. The algorithm with better accuracy will be chosen for the implementation phase.

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

Visual media has become one of the most potent means of conveying opinions or sentiments on the web. Millions of photos are being uploaded by the people on famous social networking sites for expressing themselves. The area of visual sentiment analysis is abstract in nature due to the high level of biasing in the human recognition process. This work proposes a residual attention-based deep learning network (RA-DLNet), which examines the problem of visual sentiment analysis. We aim to learn the spatial hierarchies of image features using CNN. Since the local regions in the image convey significant sentiments, we apply residual attention model, which focuses on crucial sentiment-rich, local regions in the image. The significant contribution of this work also includes an exhaustive analysis of seven popular CNN-based architectures such as VGG-16, VGG-19, Inception-Resnet-V2, Inception-V3, ResNet-50, Xception, and NASNet. The impact of fine-tuning on these CNN variants is demonstrated in visual sentiment analysis domain. The extensive experiments on eight popular benchmark data sets are conducted and the performance is measured in terms of accuracy. The comparison of accuracy with similar state-of-the-art exhibits the superiority of the proposed work.

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

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.

Sentiment Trend Analysis of Big Data

Various fields like Text Mining, Linguistics, Decision Making and Natural Language Processing together form the basis for Opinion Mining or Sentiment Analysis. People share their feelings, observations and thoughts on social media, which has emerged as a powerful tool for rapidly growing enormous repository of real time discussions and thoughts shared by people. In this paper, we aim to decipher the current popular opinions or emotions from various sources, hence, contributing to sentiment analysis domain. Text from social media, blogs and product reviews are classified according to the sentiment they project. We re-examine the traditional processes of sentiment extraction, to incorporate the increase in complexity and number of the data sources and relevant topics, while re-populating the meaning of sentiment. Working across and within numerous streams of social media, expression of sentiment and classification of polarity is re-examined, thereby redefining and enhancing the realm of sentiment. Numerous social media streams are analyzed to build datasets that are topical for each stream and are later polarized according to their sentiment expression. In conclusion, defining a sentiment and developing tools for its analysis in real time of human idea exchange is the motive.

A Multi-Layer Dual Attention Deep Learning Model With Refined Word Embeddings for Aspect-Based Sentiment Analysis

Although the sentiment analysis domain has been deeply studied in the last few years, the analysis of social media content is still a challenging task due to the exponential growth of multimedia content. Natural language ambiguities and indirect sentiments within the social media text have made it hard to classify. Aspect-based sentiment analysis creates a need to develop explicit extraction techniques using syntactic parsers to exploit the relationship between the aspect and sentiment terms. Along with the extraction approaches, word embeddings are generated through Word2Vec models for the continuous low-dimensional vector representation of text that fails to capture the significant sentiment information. This paper presents a co-extraction model with refined word embeddings to exploit the dependency structures without using syntactic parsers. For this purpose, a deep learning-based multilayer dual-attention model is proposed to exploit the indirect relation between the aspect and opinion terms. In addition, word embeddings are refined by providing distinct vector representations to dissimilar sentiments, unlike the Word2Vec model. For this, we have employed a sentiment refinement technique for pre-trained word embedding model to overcome the problem of similar vector representations of opposite sentiments. Performance of the proposed model is evaluated on three benchmark datasets of SemEval Challenge 2014 and 2015. The experimental results indicate the effectiveness of our model compared to the existing state-of-the-art models for the aspect-based sentiment analysis.

Feature extraction using CMIM for sentiment analysis

Recently, a lot of attention paid to the domain of sentiment analysis (SA), with experts acknowledging the scientific trials as well as possible applications of the processing of subjective language. SA is the computational analysis of opinions or sentiments conveyed in a body of text. The aim of SA is the detection of subjective data present in several sources and figure out the attitudes of the author regarding the topic. Features extraction looks after the identification of the features used for opinion mining, features selection is used for choosing best features for opinion classification, features weighting method is used for weighting features for good recommendations, reduction methods are used for optimisation of classification procedure. In the current study, the feature extraction is carried out term frequency/inverse document frequency and features selection through conditional mutual information maximisation (CMIM). Feature classification is done through LogitBoost, chi-squared automatic interaction detector (CHAID) as well as K-nearest neighbour (KNN) classifiers. The experimental results were contrasted with one another.

Feature extraction using CMIM for sentiment analysis

Recently, a lot of attention paid to the domain of sentiment analysis (SA), with experts acknowledging the scientific trials as well as possible applications of the processing of subjective language. SA is the computational analysis of opinions or sentiments conveyed in a body of text. The aim of SA is the detection of subjective data present in several sources and figure out the attitudes of the author regarding the topic. Features extraction looks after the identification of the features used for opinion mining, features selection is used for choosing best features for opinion classification, features weighting method is used for weighting features for good recommendations, reduction methods are used for optimisation of classification procedure. In the current study, the feature extraction is carried out term frequency/inverse document frequency and features selection through conditional mutual information maximisation (CMIM). Feature classification is done through LogitBoost, chi-squared automatic interaction detector (CHAID) as well as K-nearest neighbour (KNN) classifiers. The experimental results were contrasted with one another.