Assessing the Effectiveness of Various Text Classification Algorithms in Customer Complaint Classification: An Informative Resource for Data Scientists and Data Analysts

Semantic text classification: A survey of past and recent advances

Natural Language Processing (NLP) is a science that integrates computer knowledge, mathematical knowledge and linguistic knowledge, and text classification and recognition is considered to be an important research field and direction of natural language processing. This paper mainly studies the realization of text classification model through the processing method of text data in natural language processing and the theoretical knowledge and technical means of machine learning. We summarize the existing text classification algorithms, analyze their applicable scenarios, and optimize on the basis of these algorithm models. The paper proposes a Chinese text classification algorithm based on weight preprocessing. Algorithm based on the weight preprocessing link, so that the optimized classifier model can improve the accuracy of the existing text classifier. In this paper, the English Newsgroups corpus is used for experimental verification. The experimental results show that the classification accuracy and accuracy of the improved algorithm are better than those of the traditional text classification algorithm, thereby improving the accuracy of English text classification.

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.

Social scientists often classify text documents to use the resulting labels as an outcome or a predictor in empirical research. Automated text classification has become a standard tool since it requires less human coding. However, scholars still need many human-labeled documents for training. To reduce labeling costs, we propose a new algorithm for text classification that combines a probabilistic model with active learning. The probabilistic model uses both labeled and unlabeled data, and active learning concentrates labeling efforts on difficult documents to classify. Our validation study shows that with few labeled data, the classification performance of our algorithm is comparable to state-of-the-art methods at a fraction of the computational cost. We replicate the results of two published articles with only a small fraction of the original labeled data used in those studies and provide open-source software to implement our method.

Text classification is a procedure of categorizing a text into classes or predefined labels, allowing effective analysis and organizing the large volume of textual data. In the growth of online textual data like news, digital platforms, or social media, an effective model for text summarization and classification. However, traditional models of text classification had difficulty capturing deep semantic dependencies and contextual embeddings among texts or words, which led to misclassification and minimized classification accuracy. To overcome these challenges, the research proposed Token Level Class Importance (TLCI) and Layer Wise Learning Rate Decay (LWLRD) named as TLLRD is integrated with Bidirectional Encoder Representations from Transformers (BERT) named as (TLLRD-BERT), which is utilized for multiclass violence text classification and enhanced classification performance. The model integrates LLRD to stabilize fine-tuning across transformer layers and TLCI to emphasize semantically significant words associated with aggressive or violent expressions. The proposed TLLRD-BERT attains an accuracy of 98.24%, 96.54% and 99.71% through YouTube comments dataset, the news headline dataset and the SMS spam detection dataset, respectively.

Text classification is a powerful technique for automating assignment of documents to topic hierarchies. Although there are a number of text classification algorithms, most of them are either inefficient or too complex. We present a linear text classification algorithm called SRFW, which is fast, effective and easily used. SRFW obtains relevance factors. For new unlabelled documents, SRFW adopts sum of weights based on relevance factors to obtain the probability that these documents belong to each category and assigns them to categories that have the biggest probability. We have evaluated our algorithm on a subset of Reuters-21578 and 20-newsgroups text collections and compared it against k-NN and SVM. Experimental results show that SRFW is competitive with k nearest neighbor (k-NN) and support vector machines (SVM), while SRFW is much simpler and faster than them.

Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study

The traditional methods of English text classification have two disadvantages. One is that they cannot fully represent the semantic information of the text. The other is that they cannot fully extract and integrate the global and local information of the text. Therefore, we propose a multi-feature fusion model based on long and short term memory network and improved artificial bee colony algorithm for English text classification. In this method, the character-level vector and word-level vector representations of English text are calculated using a pre-training model to obtain a more comprehensive text feature vector representation. Then the multi-head attention mechanism is used to capture the dependencies in the text sequence to improve the semantic understanding of the text. Through feature fusion, the channel features are optimized and the spatial features and time series features are combined to improve the classification performance of the hybrid model. In the stage of network training, the weighted linear combination of maximum Shannon entropy and minimum cross entropy is used as the return degree evaluation function of the bee colony algorithm, and the scale factor is introduced to adjust the solution search strategy of leading bees and following bees, and the improved artificial bee colony algorithm is combined with the classification network to realize the automatic optimization and adjustment of network parameters. Experiments are carried out on public data set. Compared with traditional convolutional neural networks, the classification accuracy of the new model increases by 2% on average, and the accuracy of data set increases by 2.4% at the highest.

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 advent of internet has lead to colossal development of e-learning frameworks. The efficiency of such systems however relies on the effectiveness and fast content based retrieval approaches. This paper presents a methodology for efficient search and retrieval of lecture videos based on Machine Learning (ML) text classification algorithm. The text transcript is generated exclusively from the audio content extracted from the video lectures. This content is utilized for the summary and keyword extraction which is used for training the ML text classification model. An optimized search is achieved based on the trained ML model. The performance of the system is compared by training the system using Naive Bayes, Support Vector Machine and Logistic Regression algorithms. Performance evaluation was done by precision, recall, F-score and accuracy of the search for each of the classifiers. It is observed that the system trained on Naive Bayes classification algorithm achieved better performance both in terms of time and also with respect to relevancy of the search results.

Text classification based on long text and multi label text classification have always been a challenge, and the combination of these two problems brings great difficulties to text classification. This study focuses on the problem of long text multi label classification. The GLM large model was used to extract abstracts from text, which effectively reduced the length of the text and retained the main content of the text. Furthermore, the Bert-BiLSTM model was used to improve the accuracy of long sequence text. This model performs particularly well in multi label classification, and can accurately classify all category labels for multi label news classification, with much higher accuracy than conventional models.

A new text classification algorithm which is based on Ant Colony Algorithm is proposed in this paper. It makes use of the advantage in solving discrete problems by ACO and discreteness of text documents’ features. Texts are classified by crawling of class population ants which have class information with them to find an optimal path matching it during iteration in the algorithm. It can get a satisfactory classification by the experiment.

Bioinspired design is the adaptation of methods, strategies, or principles found in nature to solve engineering problems. One formalized approach to bioinspired solution seeking is the abstraction of the engineering problem into a functional need and then seeking solutions to this function using a keyword type search method on text based biological knowledge. These function keyword search approaches have shown potential for success, but as with many text based search methods, they produce a large number of results, many of little relevance to the problem in question. In this paper, we develop a method to train a computer to identify text passages more likely to suggest a solution to a human designer. The work presented examines the possibility of filtering biological keyword search results by using text mining algorithms to automatically identify which results are likely to be useful to a designer. The text mining algorithms are trained on a pair of surveys administered to human subjects to empirically identify a large number of sentences that are, or are not, helpful for idea generation. We develop and evaluate three text classification algorithms, namely, a Naïve Bayes (NB) classifier, a k nearest neighbors (kNN) classifier, and a support vector machine (SVM) classifier. Of these methods, the NB classifier generally had the best performance. Based on the analysis of 60 word stems, a NB classifier's precision is 0.87, recall is 0.52, and F score is 0.65. We find that word stem features that describe a physical action or process are correlated with helpful sentences. Similarly, we find biological jargon feature words are correlated with unhelpful sentences.

This paper studies the classification of the coal supply-demand information that comes from the Internet by using text mining technology. Firstly, it introduces the concept of Web text mining and the basic theories of the classification process. Then, it makes a further discussion about related technologies in all aspects of Web text mining process, and focuses on the analysis of the text classification algorithm. At the same time, this paper explores the current development of coal industry and the application of Web text mining on the coal supply-demand market. At last, it creates the information classification model based on the text classification algorithm of Web text mining, which visualizes the process of classifying the online coal supply-demand information.

Nowadays, investors prefer to learn the latest trends of stock market through the Internet, grasp the best time to buy and sell stocks, and simultaneously release their comments during the trading process through various online media. Therefore, many researchers utilize NLP approaches to extract sentiment tendency of investors from online comments. However, since there exists noise in corpora related to stock, when extracting emotional tendency from these corpora by using text classification, it is inevitable to face the imbalanced dataset problem, which is harmful to traditional machine learning classifiers. Our research innovatively proposes a preprocessing method based on SeqGAN algorithm, in order to solve the imbalanced classification problem. We carry out experiments on different corpora, and results show that compared with other four oversampling methods, the oversampling method based on SeqGAN can improve the performance of text classifier at best both in binary class and multiclass text classification, which proves to be an effective method for preprocessing text data.