Terms-based discriminative information space for robust text classification

Classifiers in stock market are an interesting and challenging research topic in machine learning. A large research has been conducted for classifying in stock market by using different approaches in machine learning. This research paper presents a detail study on integrating sentiment classifier and technical indicator classifier. The research subject is investigated to classify a stock into one of three labels being top, neutral or bottom. First, using technical indicators such as relative strength index (RSI), money flow index (MFI) and relative volatility index (RVI) to classify stock, then using bagging of learning machine to classify the stock. Second, using sentiment data to classify the stock. Third, integrating technical indicator and sentiment classifiers to build hybrid classifier. In this study, hybrid machine learning by combining sentiment and technical indicator classifiers is proposed. We applied this proposal hybrid classifier for five stocks in VN30. The empirical results show hybrid classifier stock has more power than single technical indicator classifier or sentiment classifier.

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.

In the past fifteen years, the grow of using Bag of Words (BoW) method in the field of computer vision is visibly observed. In addition,-for the text classification and texture recognition, it can also be used in classification of images, videos, robot localization, etc. It is one of the most common methods for the categorization of text and objects. In text classification, the BoW method records the number of occurrences of each bag that is created for each instance type or word disregarding the order of the words or the grammar. And in visual scene classification it is based on clusters of local descriptors which are taken from the images disregarding the order of the clusters. The key idea is to generate a histogram for the words in the documents or the features in the images to represent the specified document or image. The BoW method is computationally and even conceptually is simpler than many other classification methods. For that reason, BoW based systems could record new and higher performance scores on common used benchmarks of text and image classification algorithms. This paper presents an overview of BoW, importance of BoW, how does it work, applications and challenges of using BoW. This study is useful in terms of introducing the BoW method to the new researchers and providing a good background with associated related works to the researchers that are working on the model.

The research on text sentiment classification has been very popular. The main methods include the methods based on rules and lexicons, machine statistical learning, and neural network methods. These methods have achieved great success in text sentiment classification, but there are still many defects. For traditional methods, its versatility is poor, once the text style or format changes significantly, the rules need to be re-established. Although the neural network method is generally applicable, it is not sufficient for the application of language knowledge. In order to solve the above problems, we propose a new model combining the traditional method and neural network method. It combines transition lexicon, stacked BILSTM and attention mechanism, and uses word2vec to initialize word vectors. For sentiment classification, semantic features are necessary, stacked BILSTM can capture contextual semantics multiple times to obtain richer contextual information representation. At the same time, we consider that the polarity of the clauses before and after the transition words may be completely opposite, which will also have a significant impact on the classification results. Therefore, we propose two strategies for transition words. One is to use the transition lexicon to supervise the attention mechanism and make the attention model sensitive to the transition words. The other is to cut the sentence according to the transition words to eliminate the noise of irrelevant items in the sentence. Our method combines the advantages of traditional methods and neural network methods, and makes up for their respective shortcomings. We use the proposed method to conduct a sentimental binary classification experiment on three data sets. The experimental results show that our method is superior than the previous methods.

This paper considers the task of sentiment classification of subjective text across many domains, in particular on scenarios where no in-domain data is available. Motivated by the more general applicability of such methods, we propose an extensible approach to sentiment classification that leverages sentiment lexicons and out-of-domain data to build a case-based system where solutions to past cases are reused to predict the sentiment of new documents from an unknown domain. In our approach the case representation uses a set of features based on document statistics, while the case solution stores sentiment lexicons employed on past predictions allowing for later retrieval and reuse on similar documents. The case-based nature of our approach also allows for future improvements since new lexicons and classification methods can be added to the case base as they become available. On a cross domain experiment our method has shown robust results when compared to a baseline single-lexicon classifier where the lexicon has to be pre-selected for the domain in question.

Nowadays, an abundance of short text is being generated that uses nonstandard writing styles influenced by regional languages. Such informal and code-switched content are under-resourced in terms of labeled datasets and language models even for popular tasks like sentiment classification. In this work, we (1) present a labeled dataset called MultiSenti for sentiment classification of code-switched informal short text, (2) explore the feasibility of adapting resources from a resource-rich language for an informal one, and (3) propose a deep learning-based model for sentiment classification of code-switched informal short text. We aim to achieve this without any lexical normalization, language translation, or code-switching indication. The performance of the proposed models is compared with three existing multilingual sentiment classification models. The results show that the proposed model performs better in general and adapting character-based embeddings yield equivalent performance while being computationally more efficient than training word-based domain-specific embeddings.

Semantic text classification: A survey of past and recent advances

There has been a phenomenal increase in the utility of text classification (TC) in applications like targeted advertisement and sentiment analysis. Most applications demand that the model be efficient and robust, yet produce accurate categorizations. This is quite challenging as their is a dearth of labelled training data because it requires assigning labels after reading the whole document. Secondly, the people labelling the documents may not agree on a particular categorization. Therefore, pre-labelled data from a different source, domain, or a user may be used to augment (or replace) the train data. This results in the difference of distribution between the train and test sets. Additionally, with time, the distribution of the test set may change as well. This change in distribution between the train and test sets violates the inductive inference hypothesis that is underlying any machine learning (ML) prediction model, with some ML models being more sensitive towards this phenomenon than others. The performance of support vector machines (SVM) (which is one of the most successful classifiers for TC) degrades drastically in such scenarios. Therefore, in this paper we propose a novel and efficient method that uses terms-based discriminative information space to train SVM for scenarios where distribution shift exists between train and test sets. Our results on eight different train and test pairs from four different domains suggest that the performance gain achieved by SVM trained in the discriminative information space is significantly greater than the performance of SVM trained on the input feature space. Moreover, the methodology is simple, effective, and fast with a small and tunable memory footprint.

PurposeThis study aims to investigate the current state of research using deep learning methods for text classification in the tourism and hospitality field and to propose specific guidelines for future research.Design/methodology/approachThis study undertakes a qualitative and critical review of studies that use deep learning methods for text classification in research fields of tourism and hospitality and computer science. The data was collected from the Web of Science database and included studies published until February 2022.FindingsFindings show that current research has mainly focused on text feature classification, text rating classification and text sentiment classification. Most of the deep learning methods used are relatively old, proposed in the 20th century, including feed-forward neural networks and artificial neural networks, among others. Deep learning algorithms proposed in recent years in the field of computer science with better classification performance have not been introduced to tourism and hospitality for large-scale dissemination and use. In addition, most of the data the studies used were from publicly available rating data sets; only two studies manually annotated data collected from online tourism websites.Practical implicationsThe applications of deep learning algorithms and data in the tourism and hospitality field are discussed, laying the foundation for future text mining research. The findings also hold implications for managers regarding the use of deep learning in tourism and hospitality. Researchers and practitioners can use methodological frameworks and recommendations proposed in this study to perform more effective classifications such as for quality assessment or service feature extraction purposes.Originality/valueThe paper provides an integrative review of research in text classification using deep learning methods in the tourism and hospitality field, points out newer deep learning methods that are suitable for classification and identifies how to develop different annotated data sets applicable to the field. Furthermore, foundations and directions for future text classification research are set.

As one of the core tasks of natural language processing technology, text classification methods general-ly face the problems of insufficient global semantic capture and limited feature focusing ability when processing long texts or complex semantics. To address this issue, a deep learning model that integrates improved convolutional neural networks, unbalanced bidirectional long short-term memory networks, and multi-head attention mechanisms is proposed. Utilizing an improved bidirectional long short-term memory network to capture global semantic information, while dynamically focusing on key features through a multi head attention mechanism to enhance the model's adaptability to classification tasks. The performance of the model is validated through experiments on AG News (short text) and IMDb (long text) datasets. The results show that in short text classification, the proposed method has an accu-racy rate of 96% and a classification error rate of only 1.46%. In the task of long text classification, the method proposed in the study has a product under the curve of 0.98. In adversarial attack testing, the accuracy rates of adversarial samples generated by different methods are 92.85% and 90.63%, respec-tively, with the lowest robustness degradation rates of 3.72% and 5.49%, respectively. In cross domain generalization testing, it shows the least classification errors and superior cross domain adaptability. These results validate the high performance, robustness, and wide applicability of the method. The re-search indicates that this approach can validly improve the performance of text classification and pro-vide new solutions for natural language processing related tasks in long text and multi-category scenar-ios.

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.

This paper mainly deals with the classification of text type data. The statistics show that more than 8000 articles have been reached in all kinds of documents retrieved by the optical network. However, there are few papers on the factors that affect the classification of text. The text classification method used is important, but the internal factors sometimes play a great role, and even affect the success or failure of the whole text classification. In order to make up for this deficiency, this paper selects the Rocchio algorithm as the classification method, mainly from the category clustering density, class complexity, category definition, stop words and document’s length five internal factors, we tested their influences on text classification by the experiment. Experiment shows that the clustering density is higher and the complexity of the lower class, class definition is higher, the higher the accuracy of text classification, text classification effect is better, and better effect to text stop words, the length of the text does not directly affect the effect of text classification, but according to the text classification algorithm is more suitable to choose the length of the document.

The sample mean classifier, such as the nearest mean classifier (NMC) and the Bayes classifier, is not robust due to the influence of outliers. Enhancing the robust performance of these methods may result in vital information loss due to weighting or data deletion. The focus of this study is to develop robust hybrid univariate classifiers that do not rely on data weighting or deletion. The following data transformation methods, such as the least square approach (LSA) and linear prediction approach (LPA), are applied to estimate the parameters of interest to achieve the objectives of this study. The LSA and LPA estimates are applied to develop two groups of univariate classifiers. We further applied the predicted estimates from the LSA and LPA methods to develop four hybrid classifiers. These classifiers are applied to investigate whether cattle horn and base width length could be used to determine cattle gender. We also used these classification methods to determine whether shapes could classify banana variety. The NMC, LSA, LPA, and hybrid classifiers showed that cattle gender could be determined using horn length and base width measurement. The analysis further revealed that shapes could determine banana variety. The comparative results using the two data sets demonstrated that all the methods have over 90% performance prediction accuracy. The findings affirmed that the performance of the NMC, LSA, LPA, and the hybrid classifiers satisfy the data-dependent theory and are suitable for classifying agricultural products. Therefore, the proposed methods could be applied to perform classification tasks efficiently in many fields of study.

With the growing availability and popularity of online consumer reviews, people have been trying to seek sentiment-aware applications to gather and understand these opinion-rich texts. Thus, sentiment classification arises in response to analyse opinions of others automatically. In this paper, experiments of sentiment classification of Chinese online reviews across different domains are conducted by considering a couple of factors which potentially influence the sentiment classification performance. Experimental results indicate that the size of training sets and the number of features have certain influence on classification accuracy. In addition, there is no significant difference in classification accuracy when using Document Frequency, Chi-square Statistic and Information Gain, respectively, to reduce dimensionality. Low-order n-grams outperforms high-order n-grams in terms of accuracy if n-grams is taken as features. Furthermore, when words and combination of words are selected as features, the accuracy of adjectives is much close to that of NVAA (the combination of nouns, verbs, adjectives and adverbs), and is better than others as well.

“The Social Dilemma” has triggered significant online discourse, with more than 1.5 million tweets generated globally in the first month of its release and nearly 62% of them reflecting strong opinions on social media regulation. Traditional manual classification of such data is labour-intensive, errorprone, and lacks scalability, limiting the extraction of meaningful insights. To address these issues, a Natural Language Processing (NLP) pipeline is designed that preprocesses the “The Social Dilemma” Tweets dataset, followed by Exploratory Data Analysis (EDA) to identify trends, noise, and dominant language patterns. Robustly Optimized Bidirectional Encoder Representations from Transformers (RoBERTa) with word embeddings is employed for deep contextual feature extraction with SMOTEbalanced features, enhancing semantic understanding beyond surface-level text. The extracted features are used to train and evaluate multiple Machine Learning (ML) classifiers for two parallel targets: sentiment classification and topic classification. Existing approaches such as Decision Tree Classifier (DTC), K-Nearest Neighbor (KNN), and Naïve Bayes Classifier (NBC) are considered for baseline performance comparison. In the proposed methodology, a Deep Neural Network (DNN) feature extractor combined with a Tao Tree Classifier (TTC) ensures higher generalization and robust classification. Hereafter, the proposed system is named “SocialTransformDeepTree.” Sentiments are categorized into Negative, Neutral, and Positive, while topics are classified into Calls for Action, Documentary Recommendation, Emotional Reactions, Irony & Self-Reflection, and Key Quotes & Insights. This hybrid framework not only improves accuracy and computational efficiency but also establishes a scalable approach for analyzing complex social media discussions surrounding documentaries with global societal impact.