Study on feature selection and machine learning algorithms for Malay sentiment classification

Researches on sentiment analysis are growing to a great extent and attracting wide ranges of attention from academics and industries as well. Feature generation and selection are consequent for text mining as the high dimensional feature set can affect the performance of sentiment analysis. This paper exhibits the efficacy of the proposed combined feature selection technique on machine learning classification algorithms over their individual usefulness. Initially, we transform the review datasets into the feature vector of unigram features along with bi-tagged features based on POS pattern. Next, information gain (IG), Chi squared (χ2) and minimum redundancy maximum relevancy (mRMR) feature selection methods are applied to obtain an optimal feature subset for further functionality. These features are then given input to multiple machine learning classifiers, namely, support vector machine (SVM), multinomial Naive Bayes (MNB), Bernoulli Naive Bayes (BNB) and logistic regression (LR) on multi domain product review datasets. The performance of the algorithm is measured by evaluation methods such as precision, recall, and F-measure. Experimental results show that the feature selection method mRMR with SVM achieved a better accuracy of 91.39, which is encouraging and comparable to the related research.

Sentiment classification or sentiment analysis has been acknowledged as an open research domain. In recent years, an enormous research work is being performed in these fields by applying various numbers of methodologies. Feature generation and selection are consequent for text mining as the high-dimensional feature set can affect the performance of sentiment analysis. This paper investigates the inability or incompetency of the widely used feature selection methods (IG, Chi-square, and Gini Index) with unigram and bigram feature set on four machine learning classification algorithms (MNB, SVM, KNN, and ME). The proposed methods are evaluated on the basis of three standard datasets, namely, IMDb movie review and electronics and kitchen product review dataset. Initially, unigram and bigram features are extracted by applying n-gram method. In addition, we generate a composite features vector CompUniBi (unigram + bigram), which is sent to the feature selection methods Information Gain (IG), Gini Index (GI), and Chi-square (CHI) to get an optimal feature subset by assigning a score to each of the features. These methods offer a ranking to the features depending on their score; thus a prominent feature vector (CompIG, CompGI, and CompCHI) can be generated easily for classification. Finally, the machine learning classifiers SVM, MNB, KNN, and ME used prominent feature vector for classifying the review document into either positive or negative. The performance of the algorithm is measured by evaluation methods such as precision, recall, and F-measure. Experimental results show that the composite feature vector achieved a better performance than unigram feature, which is encouraging as well as comparable to the related research. The best results were obtained from the combination of Information Gain with SVM in terms of highest accuracy.

In the past decades, sentiment analysis has become one of the most active research areas in natural language processing, data mining, web mining, and information retrieval. The great demand in everyday life and the factor of novelty coupled with the availability of data from social networks have served as strong motivation for research on sentiment-analysis. A number of technical problems, most of which had not been attempted before, either in the NLP or linguistics communities have also generated strong research interests in academia. Sentiment analysis, also called opin-ion mining, is the field of study that analyzes people’s opinions, sentiments, apprais-als, attitudes, and emotions toward entities and their attributes expressed in written text. The entities can be products, services, organizations, individuals, events, issues, or topics. The field represents a large problem space. It improves not only the field of natural language processing but also management, political science, economics, and sociology because all these areas are related to the thoughts of consumers and public. User-generated content is full of opinions, because the main reason why people post messages on social media platforms is to express their views and opinions, and therefore sentiment analysis is at the centre of social media analysis. It turned out that user messages often contain plenty of sarcastic expressions and ambiguous words. Within one opinion both positive and negative sentiments can be present. This also applies to negative particles, which do not always indicate a negative tone. This article investigates four challenges faced by researchers while conducting sentiment analysis, namely: sarcasm, negation, word ambiguity, and multipolarity. These aspects significantly affect the accuracy of the results when we determine a sentiment. Modern approaches to solving the problem are also covered. These are mainly machine learning methods, such as convolutional neural networks (CNN), deep neural networks (DNN), long short-term memory (LTSM), recurrent neural network (RNN), support vector machines (SVM), etc.

Document sentiment analysis is the task of determining whether a document has a positive, negative or neutral sentiment. It is made up of subtasks including feature extraction, feature selection and sentiment classification. Feature selection is the task of selecting relevant features that can aid the classifier to produce better results. This paper focuses on comparing the classification performances based on several feature selection methods used to select relevant features and also to minimize the document-term matrix representation of the documents. The purpose of applying feature selection besides selecting relevant features is also to reduce the number of features to preserve the efficiency of the whole system. In this work, the experiment setup is designed in order to investigate the effectiveness of several selected feature selection methods in improving the sentiment analysis results. Based on the findings from the experiment, although common feature selection methods such as Document Frequency (DF), Information Gain (IG) and Chi-Squared Statistics (CSS) are found to be able to produce high sentiment analysis accuracy, the Categorical Probability Proportional Difference (CPPD) method is found to be more effective as it produces higher performance accuracy in classifying the documents based on the sentiments. Although, the Categorical Proportional Difference (CPD) method produces acceptable classification results, it is weak in reducing the number of features. In short, the CPPD method enables the sentiment analysis task to be conducted with higher accuracy rate couples with high feature reduction rate too.

Sentiment classification has become one of the most popular text classification domains especially in recent years. As it is valid for all text classification problems, high dimensionality of the feature space is one of the most important concerns for sentiment classification due to accuracy considerations. This study analyses the performance of six recent text feature selection methods for document level sentiment classification using two widely-known classifiers namely Support Vector Machines (SVM) and naive Bayes (NB). Three datasets including different types of sentiment data were utilized in the experiments. These datasets are named as Cornell movie review, Sentiment140, and Nine public sentiment. For evaluation, two different success measures namely Micro-F1 and Macro-F1 were used. Also, 3-fold cross-validation is preferred for a fair system performance evaluation. Experiments indicated that distinguishing feature selector (DFS) and discriminative features selection (DFSS) methods are superior to the other four feature selection methods for sentiment classification. The highest classification performances with SVM classifier were obtained when it is combined with DFSS feature selection method in general. On the other hand, highest classification performances with NB classifier were obtained when it is combined with DFS feature selection method.

Sentiment analysis has become increasingly important in understanding user perceptions of digital platforms. This study focuses on analyzing TikTok application reviews from the Google Play Store in Indonesia using machine learning techniques. The research aims to investigate sentiment distribution and compare the performance of three popular machine learning models: Random Forest, Support Vector Machine (SVM), and Naive Bayes. The study employed a comprehensive methodology involving data collection, preprocessing, feature extraction, and model evaluation. A dataset of 10,000 TikTok reviews was collected and preprocessed using techniques such as case folding, tokenization, and stopword removal. The sentiment labeling process categorizes reviews into positive, negative, and neutral sentiments based on user ratings. The TF-IDF algorithm was used for feature extraction, and the SMOTE technique addressed class imbalance. Results revealed a predominance of negative sentiment (53.5%), followed by neutral (32.1%) and positive (14.4%) sentiment. Model performance comparisons at different data sharing ratios (80/20 and 70/30) demonstrated that Random Forest and SVM consistently outperformed Naive Bayes. At the 80/20 ratio, Random Forest achieved the highest accuracy of 83.73%, highlighting its effectiveness in sentiment classification. The research contributes to the field of sentiment analysis and natural language processing by providing insights into user experiences with the TikTok application in Indonesia. The findings can guide application developers in understanding user perceptions and improving user experience.

The field of sentiment mining and text summarization has evoked the interest of many scientists and researchers over the last few years, as the textual data has become useful for many real-world applications and challenges. Sentiment Analysis and Opinion Mining is the most popular field for analyzing and discovering insights from text data from various sources, such as Facebook, Twitter and Amazon, Zomato, etc. It involves a computational study of an individual's behavior in terms of buying interest and then extracting his opinions on the business entity of the company. This entity can be viewed as an event, individual, blog post or product experience. Scholars in the fields of natural language processing, data mining, machine learning and others have tested a variety of methods for automating sentiment analysis. These reviews are increasing on a daily basis, as a result of which the summarization of the reviews plays a role where the text is summarized as needed, which provides useful information from a large number of reviews. It is very difficult for a human being to extract and interpret useful data from a very large file. In the text analysis, the value of sentences is decided on the basis of the linguistic characteristics of sentences. This paper provides a comprehensive review of current and past work on sentiment analysis and text description. In this research work, a new hybrid classification system is proposed based on coupling classification methods using arcing classifiers and their quality is evaluated within terms of accuracy. The Classifier Collection was constructed using Naïve Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA). The proposed work consists of a comparative study of the efficacy of the ensemble technique for sentiment classification. The feasibility and benefits of the proposed approaches are demonstrated by a restaurant review that is widely used in the field of sentiment classification. A wide range of comparative studies is performed and, ultimately, some in-depth analysis is addressed and conclusions are drawn on the efficacy of the ensemble technique for sentiment classification.

In the field of natural language processing (NLP), sentiment analysis is very important for comprehending user views and opinions. By using the Kaggle ChatGPT sentiment analysis dataset, this work investigates the use of sophisticated ensemble machine learning algorithms to enhance ChatGPT’s sentiment classification performance. Due to its skewed distribution, the dataset poses particular difficulties in reaching high performance metrics. For sentiment classification, we evaluate the performance of many boosting techniques such as AdaBoost, Gradient Boosting Machine (GBM), XGBoost, LightGBM (LGBM), and CatBoost. Moreover, ensemble techniques such as majority voting and stacking are used to improve classification results. While majority voting combines predictions for robust classification, stacking specifically uses many base learners in conjunction with a meta-model to maximize performance. With a classification accuracy of 88.57%, precision of 88.67%, recall of 88.57%, and an F-measure of 88.57%, our tests show that stacking performs better than any other technique. Its efficient mistake mitigation is further demonstrated by its False Positive Rate (FPR) of 0.09 and False Negative Rate (FNR) of 0.13. These findings highlight how ensemble methods — in particular, stacking — can be used to handle skewed data and enhance sentiment classification capabilities. In light of ChatGPT and related conversational artificial intelligence (AI) technologies, the results offer important new information for the creation of trustworthy sentiment analysis systems.

Opinion mining is the computational study of people's opinions, emotions and attitudes which is one of the key research field in Natural Language Processing (NLP). To cope with the competitive world, owners of business need to extract exact opinion of people about his/her business. Recently, people in Bangladesh are more interested to express their opinion in Bangla and most importantly in Phonetic Bangla rather than English. Since no specific work of Opinion mining introduced this criteria, in this paper, we have developed review analysis system on Bangla and Phonetic Bangla where we have used Restaurant reviews as case study and the dataset is created manually by us without using translator. Our approach starts by preprocessing raw data and then feature extraction with different N-gram techniques. Then vectorization is applied on that data with HashingVectorizer, CountVectorizer and TF-IDF vectorizer. Later machine learning based approaches namely Support Vector Machine (SVM), Decision Tree (DT) and Logistic Regression (LR) are applied to classify reviews. We have classified the reviews in three different classes, i.e. bad, good and excellent. Finally a comparison is shown between vectorizers in accordance with different classifiers where SVM provides better accuracy with 75.58%.

Sentiment Analysis (SA) and opinion mining is used for the systems of business intelligence in analyzing public opinion towards various brands and implementing market strategies. Machine learning aims at developing the algorithm in such a way that the performance of a system is optimized with past data or experience. In the case of SA, the feature selection method is used for the identification of different goals such as reduction of cost of computation, avoidance of over-fitting, and enhancing the accuracy of classification of the model. The methods of feature selection can reduce their original feature sets by the removal of irrelevant features for the classification of text sentiment and their accuracy. The paper surveys various feature selection techniques available in the literature. The study shows that feature selection significantly improves the classification of the sentiments, but it depends on the technique adopted and the number of features selected.

In the past few decades, the internet has become an inseparable part of human life. It provides ease of access and permeates almost every aspect of human existence. One of the internet platforms that is widely used by people around the world is social media. Apart from being spoiled with the convenience and efficiency offered by social media to support daily life, it has gained popularity among a wide audience. This has positive implications when utilized effectively, but it cannot be denied that there are negative consequences if not utilized properly. One such consequence is the prevalence of cyberbullying activities on social media. Cyberbullying has become a major concern for the public and social media users, prompting researchers to leverage information technology in developing technologies that can identify the elements of cyberbullying, particularly on social media platforms. Sentiment analysis has been employed by researchers to identify the components of cyberbullying in online platforms. Sentiment analysis involves the application of natural language processing techniques and text analysis to identify and extract subjective information from text. This study aims to compare the Naive Bayes algorithm and the Support Vector Machine algorithm, while utilizing feature selection, specifically chi-square, to enhance the accuracy of both algorithms in classifying Instagram comments. The experimental results indicate that the Multinomial Naive Bayes (MNB) algorithm outperforms the Support Vector Machine (SVM) algorithm, achieving an accuracy of 83.85% without feature selection and 90.77% with feature selection. Meanwhile, SVM achieves an accuracy of 82.31% without feature selection and 90% with feature selection. Evaluation through the confusion matrix and classification report reveals that MNB exhibits better precision and recall rates compared to SVM in identifying bullying and non-bullying classes. The use of feature selection enhances the performance of both algorithms in classifying Instagram comments related to cyberbullying.

Sarcasm detection is a growing field in Natural Language Processing (NLP).Sarcasm is identified using positive or more increased positive words, often with a negative connotation, to insult or mock others.In sentiment analysis, detecting sarcasm in the text has become critical.They reviewed numerous relevant research articles, but due to the telugu language's limited resources, detecting sarcasm in telugu language texts remains challenging.As a result, the sentiment detection model struggles to accurately identify the exact sentiment of a sarcastic statement, necessitating the development of an automated sarcasm detection system.Many researchers have trained and tested various machine learning classification algorithms to identify sarcasm, but these algorithms require a dataset as input, which often contains noise.

The rapid growth of financial technology (fintech) services in Indonesia has significantly transformed digital transaction behavior, with digital wallets such as DANA becoming widely adopted. Despite high usage rates, public discourse on social media frequently highlights both positive experiences and recurring concerns related to system reliability and data security. Twitter, as a real-time public communication platform, provides a rich source of user-generated content that reflects public perception toward fintech services. This study investigates public sentiment toward DANA fintech services in Indonesia through sentiment analysis of Twitter data using machine learning approaches. Purpose:This study aims to analyze public perception of DANA fintech services based on sentiment expressed on Twitter and to compare the performance of several machine learning classification algorithms in identifying positive, neutral, and negative sentiments in Indonesian-language social media texts. Methods/Study design/approach: The study employs a quantitative descriptive approach using text mining and sentiment analysis techniques. Twitter data were collected via the Twitter API over a specified period and processed through preprocessing stages including text cleaning, tokenization, stopword removal, and stemming. Feature extraction was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Sentiment classification was conducted using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest algorithms. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Result/Findings:The results indicate that public sentiment toward DANA is predominantly positive, driven by perceptions of transaction convenience and speed, followed by neutral sentiment, while negative sentiment is mainly associated with system disruptions and data security issues. Among the evaluated algorithms, SVM achieved the highest classification accuracy (91.4%), outperforming Random Forest and KNN. These findings demonstrate that SVM is more effective for sentiment classification of short Indonesian-language texts on social media platforms. Novelty/Originality/Value:This study contributes to the growing body of sentiment analysis research in Indonesia by providing empirical evidence on public perception of fintech services using machine learning techniques. By systematically comparing multiple classification algorithms in the context of Indonesian Twitter data, the study offers methodological insights and practical implications for fintech service providers in improving service quality, communication strategies, and user trust.

Sentiment Analysis (SA) research has increased tremendously in recent times. Sentiment analysis deals with the methods that automatically process the text contents and extract the opinion of the users. In this paper, unigram and bi-grams are extracted from the text, and composite features are created using them. Part of Speech (POS) based features adjectives and adverbs are also extracted. Information Gain (IG) and Minimum Redundancy Maximum Relevancy (mRMR) feature selection methods are used to extract prominent features. Further, effect of various feature sets for sentiment classification is investigated using machine learning methods. Effects of different categories of features are investigated on four standard datasets i.e. Movie review, product (book, DVD and electronics) review dataset. Experimental results show that composite features created from prominent features of unigram and bi-gram perform better than other features for sentiment classification. mRMR is better feature selection method as compared to IG for sentiment classification. Boolean Multinomial Naïve Bayes (BMNB) algorithm performs better than Support Vector Machine (SVM) classifier for sentiment analysis in terms of accuracy and execution time.KeywordsSentiment Analysisfeature selection methodsmachine learningInformation GainMinimum Redundancy Maximum Relevancy (mRMR)composite features

Sentiment analysis and sarcasm detection are specialized areas in the field of information retrieval and natural language processing. Sentiment classification is closely correlated with sarcasm detection, where people usually adopt sarcasm to highlight their negative feeling. This paper proposes a novel multi-task deep neural networks for joint sarcasm detection and sentiment analysis (MT_SS). MT_SS train both tasks jointly using bidirectional gated recurrent unit with attention network module to obtain task-specific local feature representation while using convolutional neural networks to obtain global feature representation. The experiments on two datasets show that our proposed model outperforms the state-of-the-art approaches.