Classifiers For Sentiment Analysis Research Articles

A Review of: Ensemble Feature Selection Scheme-Based Performance Evaluation of Several Classifiers for Sentiment Analysis

A Review of: Ensemble Feature Selection Scheme-Based Performance Evaluation of Several Classifiers for Sentiment Analysis

ConvBiGRU deep learning classifier for sentiment analysis with optimization algorithm

ConvBiGRU deep learning classifier for sentiment analysis with optimization algorithm

Exploring COVID-19 public perceptions in South Africa through sentiment analysis and topic modelling of Twitter posts

The narratives shared on social media during a health crisis such as COVID-19 reflect public perceptions of the crisis. This article provides findings from a study of the perceptions of South African citizens regarding the government’s response to the COVID-19 pandemic from March to May 2020. The study analysed Twitter data from posts by government officials and the public in South Africa to measure the public’s confidence in how the government was handling the pandemic. Results produced by four popular machine-learning classifiers for sentiment analysis— logistic regression (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost)—demonstrated these classifiers’ levels of effectiveness. In addition, the study used, and evaluated the effectiveness of, two topic-modelling algorithms—latent dirichlet allocation (LDA) and non-negative matrix factorisation (NMF)—in the classification of social media discourses in terms of frequently occurring topics. In terms of South African public sentiment towards COVID-19 and the government’s response, it was found that, based on the Twitter data, South Africans held predominantly negative views.

Hybrid classifier for sentiment analysis in Malayalam with modified TF-IDF features

Sentiment Analysis (SA) is a computational study that examines people’s opinions, attitudes, and opinions based on their written text. Keralites’ mother tongue, Malayalam, is the most often used language to express themselves on Twitter. As there is no automatic Sentiment Analyzer in Malayalam, the SA of Twitter messages is necessary. In this research work, a Malayalam sentiment analysis model is introduced. The input raw data in the form of reviews are fed for the pre-processing stage. The pre-processing module includes sentence tokenization via Sandi Splitting and parts of speech (POS) tagging. Subsequently, Bag of Word, the Proposed weightage-based Term Frequency-Inverse Document Frequency, Unigram with the dictionary and Unigram with dictionary including negation words have been considered for feature vector formation. Finally, the review classification is undergone via the proposed hybrid classifier, which is constructed by hybridizing the Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), respectively. To enhance the classification performance, the weight of the CNN classifier will be fine-tuned via an Improved Bumble bee mating Optimization (IBBMO), which is an advanced version of standard BBMO. The performance of the proposed work is compared over other conventional models in terms of positive, negative, convergence, and other measures. The accuracy of the proposed method is 95.436, which is much better than the existing works like DBN[Formula: see text]86.25, NN[Formula: see text]87.080, SVM[Formula: see text]87.234, WOA+Hybrid Classifier[Formula: see text]88.440, LA+Hybrid Classifier[Formula: see text]89.35285, CSO+Hybrid Classifier[Formula: see text]92.021, BBMO+Hybrid Classifier[Formula: see text]93.4248, and ML techniques[Formula: see text]94.014, at the 90th training percentage.

An automated essay evaluation system using natural language processing and sentiment analysi

<span lang="EN-US">An automated essay evaluation system is a machine-based approach leveraging long short-term memory (LSTM) model to award grades to essays written in English language. <a name="_Hlk108785338"></a>natural language processing (NLP) is used to extract feature representations from the essays. The LSTM network learns from the extracted features and generates parameters for testing and validation. The main objectives of the research include proposing and training an LSTM model using a dataset of manually graded essays with scores. Sentiment analysis is performed to determine the sentiment of the essay as either positive, negative or neutral. The twitter sample dataset is used to build sentiment classifier that analyzes the sentiment based on the student’s approach towards a topic. Additionally, each essay is subjected to detection of syntactical errors as well as plagiarism check to detect the novelty of the essay. The overall grade is calculated based on the quality of the essay, the number of syntactic errors, the percentage of plagiarism found and sentiment of the essay. The corrected essay is provided as feedback to the students. This essay grading model has gained an average quadratic weighted kappa (QWK) score of 0.911 with 99.4% accuracy for the sentiment analysis classifier.</span>

An Effective ELECTRA-Based Pipeline for Sentiment Analysis of Tourist Attraction Reviews

In the era of information explosion, it is difficult for people to decide on a tourist destination quickly. Online travel review texts provide valuable references and suggestions to assist in decision making. However, tourist attraction reviews are primarily informal and noisy. Most works in this field focus on shallow machine learning models or non-pretrained deep learning models. These approaches struggle to generate satisfactory classification results. To solve this issue, the paper proposes a pipeline model. In the first step of this paper, we preprocess tourist attraction reviews by performing stopword removal, special character removal, redundancy deletion and negation substitution to reduce noise. Then, we propose an ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) classifier for sentiment analysis of tourist attraction review. Finally, we compare our pipeline model with several representative deep text classification models. Extensive experiments have demonstrated the effectiveness of our approach to sentiment analysis of tourist attraction reviews. We not only provide one high-quality dataset for tourist attraction reviews, but our work can also expand and promote the development of sentiment analysis in other domains.

Sentimental Analysis of Industry 4.0 Perspectives Using a Graph-Based Bi-LSTM CNN Model

Today’s modern society mainly depends on Internet for every fundamental task in their life, like sharing thoughts, education, business, and Industry 4.0, etc. Internet has strengthened the base of society digitally. Searching for the reviews and comments for a particular product from the former or present customers has become compulsory for making decision or a purchase, helping them to make a fair deal of the product by view from social media on Industry 4.0. The increased use of data over Internet has led to rise of many e-commerce websites where people can buy things as per their requirement without even stepping out of their house using analysis of text using natural language techniques in Industry 4.0. The graph-based modelling of sentiment analysis classifier needs to divide the textual data into training dataset and testing dataset. First, preprocessing work is performed to improve the data reliability by removing unwanted information and fixing typing errors. To train the models, the whole dataset is used and to measure the classification performance of the categorized models 10-fold validation technique is being used. The paper proposes a combined hybrid model for sentiment analysis using CNN and independent bidirectional LSTM networks to enhance sentiment knowledge in order to address the issues mentioned for sentiment analysis. The proposed CNN model uses global max-pooling for retrieving context information and to downsample the dimensionality. Lastly, to acquire long term dependencies, a distinctive bidirectional LSTM is used. To emphasise each word's learning ability, parts-of-speech (PoS) are tagged in the LSTM layer. In addition, the regularization techniques, batch normalization, and dropout are used to prevent the overfitting issue. The proposed model is compared with a collaborative classifier with six classifiers and each of them predicts the sentiments separately, and the majority class prediction is taken under consideration. The proposed Bi-LSTM CNN model achieves an accuracy of 98.61% along with PoS tagging of the sentiments.

Chinese public opinion on Japan's nuclear wastewater discharge: A case study of Weibo comments based on a thematic model

Japan's plan to dump nuclear wastewater into the sea has generated a tremendous amount of discussion on social media due to the potential wide-ranging impact. To our knowledge, few studies have mined social media platforms to assess similar pollution concerns. We use the Octopus Collector to collect online textual data regarding “Japan's plan to dump nuclear wastewater into the sea” from Sina Weibo since April 13, 2021. After the posts from Sina Weibo were preprocessed, user opinions were analyzed using natural language processing. We used a naive Bayes classifier for sentiment analysis and latent dirichlet allocation (LDA) to extract and cluster topics from the posts, allowing for users' related opinions to be mined and analyzed. The study found that there were three major themes in terms of public concern: nuclear pollution and marine ecology, seafood imports and food safety, and international responsibility and public ethics. In our emotional analysis, we found that most people expressed negative emotions about the plan. However, there was also a positive emotional aspect because, with the release of relevant information and the popularization of knowledge, the public has been able to have a rational discussion about the consequences of this event, and the topic includes a focus on positive factors such as environmental protection and sustainable development. For this reason, the government and relevant agencies should keep up-to-date with the latest news of the incident to further raise public awareness and lead the public to a rational discussion to avoid excessive negative emotions.

An ensemble deep learning classifier for sentiment analysis on code-mix Hindi-English data.

Code-mixing on social media is a trend in many countries where people speak multiple languages, such as India, where Hindi and English are major communication languages. Sentiment analysis is beneficial in understanding users’ opinions and thoughts on social, economic, and political issues. It eliminates the manual monitoring of each and every review, which is a cumbersome task. However, performing sentiment analysis on code-mix data is challenging, as it involves various out of vocabulary terms and numerous issues, making it a new field in natural language processing. This work includes dealing with such text and ensembling a classifier to detect sentiment polarity. Our classifier ensembles a multilingual variant of RoBERTa and a sentence-level embedding from Universal Sentence Encoder to identify the sentiments of these code-mixed tweets with higher accuracy. This ensemble optimises the classifier's performance by using the strength of both for transfer learning. Experiments were conducted on real-life benchmark datasets and revealed their sentiment. The performance of the proposed classifier framework is compared with other baselines and deep learning models on five datasets to show the superiority of our results. Results showed improved and increased performance in the proposed classifier's accuracy, precision, and recall. The accuracy achieved by our classifier on code-mix datasets is 66% on Joshi et al. 2016, 60% on SAIL 2017, and 67% on SemEval 2020 Task-9 dataset, which is on average around 3% as compared to contemporary baselines.

Empirical Evaluation of Shallow and Deep Learning Classifiers for Arabic Sentiment Analysis

This work presents a detailed comparison of the performance of deep learning models such as convolutional neural networks, long short-term memory, gated recurrent units, their hybrids, and a selection of shallow learning classifiers for sentiment analysis of Arabic reviews. Additionally, the comparison includes state-of-the-art models such as the transformer architecture and the araBERT pre-trained model. The datasets used in this study are multi-dialect Arabic hotel and book review datasets, which are some of the largest publicly available datasets for Arabic reviews. Results showed deep learning outperforming shallow learning for binary and multi-label classification, in contrast with the results of similar work reported in the literature. This discrepancy in outcome was caused by dataset size as we found it to be proportional to the performance of deep learning models. The performance of deep and shallow learning techniques was analyzed in terms of accuracy and F1 score. The best performing shallow learning technique was Random Forest followed by Decision Tree, and AdaBoost. The deep learning models performed similarly using a default embedding layer, while the transformer model performed best when augmented with araBERT.

An Effective Text Classifier using Machine Learning for Identifying Tweets’ Polarity Concerning Terrorist Connotation

Terrorist groups in the Arab world are using social networking sites like Twitter and Facebook to rapidly spread terror for the past few years. Detection and suspension of such accounts is a way to control the menace to some extent. This research is aimed at building an effective text classifier, using machine learning to identify the polarity of the tweets automatically. Five classifiers were chosen, which are AdB_SAMME, AdB_SAMME.R, Linear SVM, NB, and LR. These classifiers were applied on three features namely S1 (one word, unigram), S2 (word pair, bigram), and S3 (word triplet, trigram). All five classifiers evaluated samples S1, S2, and S3 in 346 preprocessed tweets. Feature extraction process utilized one of the most widely applied weighing schemes tf-idf (term frequency-inverse document frequency).The results were validated by four experts in Arabic language (three teachers and an educational supervisor in Saudi Arabia) through a questionnaire. The study found that the Linear SVM classifier yielded the best results of 99.7 % classification accuracy on S3 among all the other classifiers used. When both classification accuracy and time were considered, the NB classifier demonstrated the performance on S1 with 99.4% accuracy, which was comparable with Linear SVM. The Arab world has faced massive terrorist attacks in the past, and therefore, the research is highly significant and relevant due to its specific focus on detecting terrorism messages in Arabic. The state-of-the-art methods developed so far for tweets classification are mostly focused on analyzing English text, and hence, there was a dire need for devising machine learning algorithms for detecting Arabic terrorism messages. The innovative aspect of the model presented in the current study is that the five best classifiers were selected and applied on three language models S1, S2, and S3. The comparative analysis based on classification accuracy and time constraints proposed the best classifiers for sentiment analysis in the Arabic language.

Comparative study of various approaches, applications and classifiers for sentiment analysis

In today's day and age, there are billions of volumes of textual content being generated everywhere. In-apps messages like WhatsApp, Telegram, social media sites like Facebook, Instagram, news publishing sites, google searches and many other sources. All these sources are constantly generating huge volumes of text data every second. And because of these huge volumes of text data NLP becomes a vital resource in understanding the textual content. In this paper, the main focus is on the popular NLP task of Sentiment analysis. Sentiment analysis is contextual mining of text which identifies and extracts subjective information in textual data. sentiment analysis proves to be an incredible asset for users to extract essential information and assists organizations with understanding the social sentiment of their brand, product or service while monitoring online conversations. This paper investigates the different approaches and classification models used in the task of sentiment analysis.

Design of adaptive ensemble classifier for online sentiment analysis and opinion mining.

DataStream mining is a challenging task for researchers because of the change in data distribution during classification, known as concept drift. Drift detection algorithms emphasize detecting the drift. The drift detection algorithm needs to be very sensitive to change in data distribution for detecting the maximum number of drifts in the data stream. But highly sensitive drift detectors lead to higher false-positive drift detections. This paper proposed a Drift Detection-based Adaptive Ensemble classifier for sentiment analysis and opinion mining, which uses these false-positive drift detections to benefit and minimize the negative impact of false-positive drift detection signals. The proposed method creates and adds a new classifier to the ensemble whenever a drift happens. A weighting mechanism is implemented, which provides weights to each classifier in the ensemble. The weight of the classifier decides the contribution of each classifier in the final classification results. The experiments are performed using different classification algorithms, and results are evaluated on the accuracy, precision, recall, and F1-measures. The proposed method is also compared with these state-of-the-art methods, OzaBaggingADWINClassifier, Accuracy Weighted Ensemble, Additive Expert Ensemble, Streaming Random Patches, and Adaptive Random Forest Classifier. The results show that the proposed method handles both true positive and false positive drifts efficiently.

Self-Harm Detection for Mental Health Chatbots.

Chatbots potentially address deficits in availability of the traditional health workforce and could help to stem concerning rates of youth mental health issues including high suicide rates. While chatbots have shown some positive results in helping people cope with mental health issues, there are yet deep concerns regarding such chatbots in terms of their ability to identify emergency situations and act accordingly. Risk of suicide/self-harm is one such concern which we have addressed in this project. A chatbot decides its response based on the text input from the user and must correctly recognize the significance of a given input. We have designed a self-harm classifier which could use the user's response to the chatbot and predict whether the response indicates intent for self-harm. With the difficulty to access confidential counselling data, we looked for alternate data sources and found Twitter and Reddit to provide data similar to what we would expect to get from a chatbot user. We trained a sentiment analysis classifier on Twitter data and a self-harm classifier on the Reddit data. We combined the results of the two models to improve the model performance. We got the best results from a LSTM-RNN classifier using BERT encoding. The best model accuracy achieved was 92.13%. We tested the model on new data from Reddit and got an impressive result with an accuracy of 97%. Such a model is promising for future embedding in mental health chatbots to improve their safety through accurate detection of self-harm talk by users.

Adversarial attacks on a lexical sentiment analysis classifier

Social media has become a relevant information source for several decision-making processes and for the definition of business strategies. As various sentiment analysis techniques are used to transform collected data into intelligence information, the sentiment classifiers used in these collection environments must be carefully studied and observed before being considered trustful and ready to be installed in decision support systems. An important research area concerns the robustness of sentiment classifiers in view of new adversarial attacks, in which small perturbations may be created by malicious users to deceive the sentiment classifiers, generating a perception different from the one that should be observed in the environment. Thus, it is important to identify and analyze the vulnerabilities of these classifiers under different strategies of adversarial attacks to propose countermeasures that can be used to mitigate such attacks. In this context, this work presents adversarial attacks related to a lexical natural language classifier. Being the target of the attacks, this classifier is used to calculate the sentiment of collected data as posted by users in various social media applications. The results indicate that the found vulnerabilities, if exploited by malicious users in applications that use the same lexical classifier, could invert or cancel the classifiers’ perception, thus generating perceptions that do not correspond to the reality for decision making. This work also proposes some countermeasures that might mitigate the implemented attacks.

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.

The application of k-nearest neighbors classifier for sentiment analysis of PT PLN (Persero) twitter account service quality

Social media has an important role in human life. In its implementation social media is used as a media for opinion and self-expression. One of the social media that is often used in Indonesia is Twitter. PT PLN (Persero) as a State-Owned Enterprise that is engaged in providing electricity always tries to provide optimal services. The text mining method can be used to control PT PLN (Persero) service quality by classifying Twitter data with the k-Nearest Neighbors algorithm. Text mining is used to extract information from unstructured textual data to produce useful information. Data classification is a text mining application for information retrieval. In this study the data collected will pass the preprocessing stage, using the k-Nearest Neighbors algorithm to classify data into negative, neutral, or positive classes. The data used in this study was sourced from Twitter. Data is taken from 1 December 2019 to 1 February 2020 using the Twitter API with the keyword ‘@ pln_123’. We obtained 3,000 tweet data successfully. The results are implemented in web-based applications that are built using the Python programming language. Evaluation of the k-Nearest Neighbors model produces an accuracy value of 87.41%. The classification prediction results also show that there is a tendency of the positive sentiment of 35%, the neutral sentiment of 28%, and the negative sentiment of 37%.

Knowledge-Guided Sentiment Analysis Via Learning From Natural Language Explanations

Sentiment analysis is crucial for studying public opinion since it can provide us with valuable information. Existing sentiment analysis methods rely on finding the sentiment element from the content of user-generated. However, the question of why a message produces certain emotions has not been well explored or utilized in previous works. To address this challenge, we propose a natural language explanation framework for sentiment analysis that provides sufficient domain knowledge for generating additional labelled data for each new labelling decision. A rule-based semantic parser transforms these explanations into programmatic labelling functions that generate noisy labels for an arbitrary amount of unlabelled sentiment information to train a sentiment analysis classifier. Experiments on two sentiment analysis datasets demonstrate the superiority it achieves over baseline methods by leveraging explanations as external knowledge to joint training a sentiment analysis model rather than only labels. An ablation study is conducted to clarify the relative contribution of natural language explanations.

Sentiment Classification Based on Public Reviews of Different States Towards the Police

Sentiment analysis can be referred to as an Opinion Mining which is an exemplary example of machine learning and is a significant element of NLP that is agitated with the persistence of opinion and individuality in a text, which also includes many applications. This analysis not only identifies the opinions but also extracts the attributes for the expressions. This research will enlighten and set forth the facts and statistics about classifiers for sentiment analysis of public opinion towards police working in different states through comments and tweets via Support Vector Machine (SVM). The proposed with the help of NLP classifies the text into positive and negative tweets where processes like stemming and tokenisation are used to remove the unwanted characters and patterns that are neither positive nor negative in the document. Usage of logistic regression is intended to classify the document into sentence level to consider the polarity of the document. The model has indicates to extract the features from the dataset used and extract the positive and negative tweets as to how they feel about the working of the police in their states. Through SVM and Logistic regression we intend to find the accurate score for the analysis that is classified from the input text to the classes of interest which specifies the need to use the keywords mentioned. After training the model results show that Delhi is the one of the top state with less working of the police whereas Chennai becomes the state where the work of the police is appreciated by their citizen’s which is shown with the help of pie and bar chart. Comparing the accuracies of logistic regression and the SVM techniques accuracies, SVM accuracy made out to be better with 55.4% and for logistic regression gives 56.7% accuracy.

Can You Tell? Ssnet - a Sagittal Stratum-Inspired Neural Network Framework for Sentiment Analysis

When people try to understand nuanced language they typically process multiple input sensor modalities to complete this cognitive task. It turns out the human brain has even a specialized neuron formation, called sagittal stratum, to help us understand sarcasm. We use this biological formation as the inspiration for designing a neural network architecture that combines predictions of different models on the same text to construct a robust, accurate and computationally efficient classifier for sentiment analysis. Experimental results on representative benchmark datasets and comparisons to other methods show the advantages of the new network architecture.