Text Streams Research Articles

Technological and Innovative Transformation in Marketing: Examination of Social Media Text Stream Visualization Concept with Bibliometric Analysis: Future Agenda

Changes in technology and innovation processes affect many areas. The impact of technology and innovative applications lead to radical changes in marketing strategies. Social media text stream visualization stands out as one of the most important tools of these changes. Social media text stream visualization allows the analysis of text data coming from social media platforms through graphics and infographics. In this way, social media text stream visualization allows the development of more effective and efficient strategies by combining technology and innovation in marketing. In this study, the Web of Science database is used to reveal in which areas the concept of social media text stream visualization has been used in the last 10 years through bibliometric analysis. After the inclusion and exclusion criteria are applied, the final sample consists of 12 refereed articles. Five separate analyses were conducted to test the sample. Performance analysis determined the publication years of the articles, their contributions by country, the outputs of relevant journals, and the sectors where this concept is used most intensively. The findings of the study provide a broad perspective on the research conducted to date and identify potential research gaps. Accordingly, it is seen that the studies within the scope of social media text stream visualization are mostly conducted in the fields of social media, advertising, ecommerce and market research. It was concluded that machine learning, big data, text analysis and time series methods were used in the studies. It is recommended that studies be conducted in the retail, tourism and accommodation and technology sectors for future research. This research aims to contribute to the field of marketing by revealing the gaps in the literature. The study also offers suggestions for future research to be conducted by academics and sectoral researchers.

Soft Prompt-tuning with Self-Resource Verbalizer for short text streams

Short text streams such as real-time news and search snippets have attained vast amounts of attention and research in recent decades, the characteristics of high generation velocity, feature sparsity, and high ambiguity accentuate both the importance and challenges to language models. However, most of the existing short text stream classification methods can neither automatically select relevant knowledge components for arbitrary samples, nor expand knowledge internally instead of rely on external open knowledge base to address the inherent limitations of short text stream. In this paper, we propose a Soft Prompt-tuning with Self-Resource Verbalizer (SPSV for short) for short text stream classification, the soft prompt with self-resource knowledgeable expansion is conducted for updating label words space to address evolved semantic topics in the data streams. Specifically, the automatic constructed prompt is first generated to instruct the model prediction, which is optimized to address the problem of high velocity and topic drift in short text streams. Then, in each chunk, the projection between category names and label words space, i.e. verbalizer, is updated, which is constructed by internal knowledge expansion from the short text itself. Through comprehensive experiments on four well-known benchmark datasets, we validate the superb performance of our method compared to other short text stream classification and fine-tuning PLMs methods, which achieves up to more than 90% classification accuracy with the counts of data chunk increased.

Detection and treatment of string events in the limit

A string event is a pattern that occurs in a stream of characters. The need to detect and handle string events in infinite texts emerges in many scenarios, including online treatment of logs, web crawling, and syntax highlighting. This paper describes a technique to specify and treat string events. Users determine patterns of interest via a markup language. From such examples, tokens are generalized via a semi-lattice of regular expressions. Such tokens are combined into a context-free language that recognizes patterns in the text stream. These techniques are implemented in a text processing system called Lushu, which runs on the Java Virtual Machine (JVM). Lushu intercepts strings emitted by the JVM. Once patterns are detected, it invokes a user-specified action handler. As a proof of concept, this paper shows that Lushu outperforms state-of-the-art parsers and parser generators, such as Comby, BeautifulSoup4 and ZheFuscator, in terms of memory consumption and running time.

AIoT based sentiment analysis via multi‐scale context perception to enhance linguistic teaching

AbstractWith the rapid development of the Internet of Things (IoT) and artificial intelligence technology, real‐time text sentiment analysis plays an important role in online education. Due to the limited resources of clients, existing deep networks cannot be directly deployed in the edge node. In addition, deep convolutional networks cannot fully utilize contextual information. In order to resolve these issues, this paper proposes a multi‐scale context‐aware text sentiment analysis system based on cloud computing, in which a bidirectional long short‐term memory network (BiLSTM) model is deployed in the cloud server. The BiLSTM model can fully explore the contextual feature information of the text stream in online education. The real‐time text data are collected through terminal nodes, such as pad or computer, to send the cloud server. The experiments utilize three public text datasets to simulate the input of terminal nodes. The results show that the proposed system shows better accuracy than previous models and can return the emotional status in time.

A topic-enhanced dirichlet model for short text stream clustering

A topic-enhanced dirichlet model for short text stream clustering

A Novel Neural Ensemble Architecture for On-the-fly Classification of Evolving Text Streams

We study on-the-fly classification of evolving text streams in which the relation between the input data and target labels changes over time—i.e., “concept drift.” These variations decrease the model’s performance, as predictions become less accurate over time and they necessitate a more adaptable system. While most studies focus on concept drift detection and handling with ensemble approaches, the application of neural models in this area is relatively less studied. We introduce Adaptive Neural Ensemble Network ( AdaNEN ), a novel ensemble-based neural approach, capable of handling concept drift in data streams. With our novel architecture, we address some of the problems neural models face when exploited for online adaptive learning environments. Most current studies address concept drift detection and handling in numerical streams, and the evolving text stream classification remains relatively unexplored. We hypothesize that the lack of public and large-scale experimental data could be one reason. To this end, we propose a method based on an existing approach for generating evolving text streams by introducing various types of concept drifts to real-world text datasets. We provide an extensive evaluation of our proposed approach using 12 state-of-the-art baselines and 13 datasets. We first evaluate concept drift handling capability of AdaNEN and the baseline models on evolving numerical streams; this aims to demonstrate the concept drift handling capabilities of our method on a general spectrum and motivate its use in evolving text streams. The models are then evaluated in evolving text stream classification. Our experimental results show that AdaNEN consistently outperforms the existing approaches in terms of predictive performance with conservative efficiency.

The (Not-So-)Private Mind: Why Faulkner’s The Sound and the Fury Is and Is Not a Failure

Abstract: Faulkner’s The Sound and the Fury attempts to represent first-person experience in a radical fashion. In what I call (paradoxically) “free indirect discourse in the first person,” Faulkner ostensibly presents both thought thought and thought below the level of awareness together in one stream of text. The Quentin section in particular relies on an idealistic picture of language as meaningful in itself, apart from any intersubjective context or significant use, as though we could bypass communication and look inside Quentin’s head, finding not the brain but the “exact language” of his conscious life. I consider this temptation by way of Wittgenstein’s critique of the privacy of the mental. Wittgenstein’s aim, I argue, is not to deny or demote interiority, but rather to impugn a certain picture of how “the inner” must look — a realm composed of private objects to which the “I” alone has access. I thus suggest that we think of Quentin as an experiment, an appeal. Faulkner tries to reveal a mind in the brutal fullness of its suffering without forcing that mind to address us: to tell us that they suffer . I contend that this appeal fails, and in failing reveals the manner in which the (not-so-private) mind is essentially embedded in a shared, intersubjective world.

Continuous Similarity Search for Dynamic Text Streams

Continuous Similarity Search for Dynamic Text Streams

Context-Based Persuasion Analysis of Sentiment Polarity Disambiguation in Social Media Text Streams

Context-Based Persuasion Analysis of Sentiment Polarity Disambiguation in Social Media Text Streams

Rumor identification and diffusion impact analysis in real-time text stream using deep learning

Rumor identification and diffusion impact analysis in real-time text stream using deep learning

RESEARCH APPLICATION OF THE SPAM FILTERING AND SPAMMER DETECTION ALGORITHMS ON SOCIAL MEDIA AND MESSENGERS

In the current era, numerous social networks and messaging platforms have become integral parts of our lives, particularly in relation to work activities, due to the prevailing COVID-19 pandemic and russian war in Ukraine. Amidst this backdrop, the issue of spam and spammers has become more pertinent than ever, with a continuous rise in the incidence of spam within work-related text streams. Spam refers to textual content that is extraneous to a specific text stream, while a spammer denotes an individual who disseminates unsolicited messages for personal gain. The proposed article is devoted to address this scientific and practical challenge of identifying spammers and detecting spam messages within the textual context of any social network or messenger. This endeavor encompasses the utilization of diverse spam detection algorithms and approaches for spammer identification. Four algorithms were implemented, namely a naive Bayesian classifier, Support-vector machine, multilayer perceptron neural network, and convolutional neural network. The research objective was to develop a spam detection algorithm that can be seamlessly integrated into a messenger platform, exemplified by the utilization of Telegram as a case study. The designed algorithm discerns spam based on the contextual characteristics of a specific text stream, subsequently removing the spam message and blocking the spammer-user until authorized by one of the application administrators.

Accelerating Aggregation Queries on Unstructured Streams of Data

Analysts and scientists are interested in querying streams of video, audio, and text to extract quantitative insights. For example, an urban planner may wish to measure congestion by querying the live feed from a traffic camera. Prior work has used deep neural networks (DNNs) to answer such queries in the batch setting. However, much of this work is not suited for the streaming setting because it requires access to the entire dataset before a query can be submitted or is specific to video. Thus, to the best of our knowledge, no prior work addresses the problem of efficiently answering queries over multiple modalities of streams. In this work we propose InQuest, a system for accelerating aggregation queries on unstructured streams of data with statistical guarantees on query accuracy. InQuest leverages inexpensive approximation models ("proxies") and sampling techniques to limit the execution of an expensive high-precision model (an "oracle") to a subset of the stream. It then uses the oracle predictions to compute an approximate query answer in real-time. We theoretically analyzed InQuest and show that the expected error of its query estimates converges on stationary streams at a rate inversely proportional to the oracle budget. We evaluated our algorithm on six real-world video and text datasets and show that InQuest achieves the same root mean squared error (RMSE) as two streaming baselines with up to 5.0x fewer oracle invocations. We further show that InQuest can achieve up to 1.9x lower RMSE at a fixed number of oracle invocations than a state-of-the-art batch setting algorithm.

PEN: Prediction-Explanation Network to Forecast Stock Price Movement with Better Explainability

Nowadays explainability in stock price movement prediction is attracting increasing attention in banks, hedge funds and asset managers, primarily due to audit or regulatory reasons. Text data such as financial news and social media posts can be part of the reasons for stock price movement. To this end, we propose a novel framework of Prediction-Explanation Network (PEN) jointly modeling text streams and price streams with alignment. The key component of the PEN model is an shared representation learning module that learns which texts are possibly associated with the stock price movement by modeling the interaction between the text data and stock price data with a salient vector characterizing their correlation. In this way, the PEN model is able to predict the stock price movement by identifying and utilizing abundant messages while on the other hand, the selected text messages also explain the stock price movement. Experiments on real-world datasets demonstrate that we are able to kill two birds with one stone: in terms of accuracy, the proposed PEN model outperforms the state-of-art baseline; on explainability, the PEN model are demonstrated to be far superior to attention mechanism, capable of picking out the crucial texts with a very high confidence.

A context-enhanced Dirichlet model for online clustering in short text streams

Online clustering of short text streams has become significant due to the popularity of news and social media platforms. The objective of online clustering is to maintain active topics (clusters) by automatically detecting new topics and forgetting outdated ones. Most existing approaches exploit static and high dimensional semantic term representation of the text to enhance the clustering quality. While these approaches use inference procedures that depend on a fixed batch size to reduce the number of clusters related to a given topic and bring it closer to the actual number of topics. This paper proposes a non-parametric Dirichlet model with episodic inference (EINDM) to cluster the evolving short text stream by introducing a window-based low-dimensional semantic term representation which captures the contextual relationships between words. In addition, an episodic inference procedure is introduced to reduce the cluster sparsity in the model. Furthermore, a novel “word specificity” measure is proposed based on neighborhood terms for evolving contexts for individual terms. Extensive empirical evaluation demonstrates that EINDM yields the best performance, in terms of NMI, homogeneity, and cluster purity, compared to recent state-of-the-art clustering models.

Recent methods on short text stream clustering: A survey study

AbstractThe volume and the velocity of data in social media are increasing and the social media has become a very useful environment to detect and track the real‐world events. However, to fulfill this, it is crucial to group‐related texts according to their topics and clustering takes an essential role at this point since we have no prior knowledge about the topics and their evolution in social media. In this survey, we review the current approaches and techniques proposed for short text stream clustering in recent years. The reviewed techniques are grouped according to their methodology and discussed in detail. Also, the datasets utilized to evaluate the performance of the proposed methods and the results are summarized together with the clustering quality measures used for these evaluations. Furthermore, current challenges about short‐text stream clustering are discussed.This article is categorized under: Data: Types and Structure > Streaming Data

Exploiting the Self-Attention Mechanism in Gas Sensor Array (GSA) Data With Neural Networks

Gas sensor array (GSA) data is a sequential series of values that represents the temporal conditions of the existence/absence/mixture of gases and exhibits similarities to the textual stream of natural languages that represents semantic information. We speculate and subsequently prove that there also exist self-attention mechanisms in GSA data that can be exploited for gas classification and recognition. We first convert GSA data into a 1-D token series (called WORDs in this work) through sampling and quantization of the sensor values and then use an enhanced long short-term memory (LSTM) revision network, called LSTM-attention, to extract the self-attention mechanism in the GSA data. We demonstrate that LSTM-attention achieves a much better performance (99.6%) than CNN-based networks as well as other GSA data process techniques on UCI dynamic gases dataset. We also find out that the self-attention mechanism varies with different sampling and quantization levels during data acquisition.

A Drift-Sensitive Distributed LSTM Method for Short Text Stream Classification

Real-world applications especially in the fields of social media have produced massive short text streams. Unlike traditional normal texts, these data present the characteristics of short length, high-volume, high-velocity and variable data distribution etc, which lead to the issues of data sparsity and concept drift. It is hence very challenging for existing short text classification algorithms. Therefore, we propose a flexible Long Short-Term Memory (LSTM) ensemble network based short text stream classification approach, which is implemented in a distributed mode while maintaining the high-accuracy advantage of deep learning models. More specifically, external resource based short text embedding using a pretrained embedding model and CNN is first proposed for the solution to the data sparsity of short texts. Second, to adapt to the high-volume and high-velocity short text streams, a flexible LSTM network is developed and implemented in a distributed mode for classifying short text data streams. Third, a concept drift factor is introduced for adapting to the concept drifts caused by the changing of data distributions. Finally, experiments conducted on three real short text data sets demonstrate that as compared with several state-of-the-art short text (stream) classification approaches, the proposed approach can classify short text streams effectively and efficiently while adapting to concept drifts.

Using Primary Care Clinical Text Data and Natural Language Processing to Identify Indicators of COVID-19 in Toronto, Canada.

The objective of this study was to investigate whether a rule-based natural language processing (NLP) system, applied to primary care clinical text data, could be used to monitor COVID-19 viral activity in Toronto, Canada. We employed a retrospective cohort design. We included primary care patients with a clinical encounter between January 1, 2020 and December 31, 2020 at one of 44 participating clinical sites. During the study timeframe, Toronto first experienced a COVID-19 outbreak between March-2020 and June-2020; followed by a second viral resurgence from October-2020 through December-2020. We used an expert derived dictionary, pattern matching tools and contextual analyzer to classify primary care documents as 1) COVID-19 positive, 2) COVID-19 negative, or 3) unknown COVID-19 status. We applied the COVID-19 biosurveillance system across three primary care electronic medical record text streams: 1) lab text, 2) health condition diagnosis text and 3) clinical notes. We enumerated COVID-19 entities in the clinical text and estimated the proportion of patients with a positive COVID-19 record. We constructed a primary care COVID-19 NLP-derived time series and investigated its correlation with independent/external public health series: 1) lab confirmed COVID-19 cases, 2) COVID-19 hospitalizations, 3) COVID-19 ICU admissions, and 4) COVID-19 intubations. A total of 196,440 unique patients were observed over the study timeframe, of which 4,580 (2.3%) had at least one positive COVID-19 document in their primary care electronic medical record. Our NLP-derived COVID-19 time series describing the temporal dynamics of COVID-19 positivity status over the study timeframe demonstrated a pattern/trend which strongly mirrored that of other external public health series under investigation. We conclude that primary care text data passively collected from electronic medical record systems represent a high quality, low-cost source of information for monitoring/surveilling COVID-19 impacts on community health.

An Online Semantic-Enhanced Graphical Model for Evolving Short Text Stream Clustering.

Due to the popularity of social media and online fora, such as Twitter, Reddit, Facebook, and Wechat, short text stream clustering has gained significant attention in recent years. However, most existing short text stream clustering approaches usually work on static data and tend to cause a "term ambiguity" problem due to the sparse word representation. Beyond, they often exploit short text streams in a batch way and are difficult to find evolving topics in term-changing subspaces. In this article, we propose an online semantic-enhanced graphical model for evolving short text stream clustering (OSGM), by exploiting the word-occurrence semantic information and dynamically maintaining evolving active topics in term-changing subspaces in an online way. Compared to the existing approaches, our online model is not only free of determining the optimal batch size but also lends itself to handling large-scale data streams efficiently. It is also able to handle the "term ambiguity" problem without incorporating features from external resources. More importantly, to the best of our knowledge, it is the first work to extract evolving topics in term-changing subspaces automatically in an online way. Extensive experiments demonstrate that the proposed model yields better performance compared to many state-of-the-art algorithms on both synthetic and real-world datasets.

Effective Model Update for Adaptive Classification of Text Streams in a Distributed Learning Environment

In this study, we propose dynamic model update methods for the adaptive classification model of text streams in a distributed learning environment. In particular, we present two model update strategies: (1) the entire model update and (2) the partial model update. The former aims to maximize the model accuracy by periodically rebuilding the model based on the accumulated datasets including recent datasets. Its learning time incrementally increases as the datasets increase, but we alleviate the learning overhead by the distributed learning of the model. The latter fine-tunes the model only with a limited number of recent datasets, noting that the data streams are dependent on a recent event. Therefore, it accelerates the learning speed while maintaining a certain level of accuracy. To verify the proposed update strategies, we extensively apply them to not only fully trainable language models based on CNN, RNN, and Bi-LSTM, but also a pre-trained embedding model based on BERT. Through extensive experiments using two real tweet streaming datasets, we show that the entire model update improves the classification accuracy of the pre-trained offline model; the partial model update also improves it, which shows comparable accuracy with the entire model update, while significantly increasing the learning speed. We also validate the scalability of the proposed distributed learning architecture by showing that the model learning and inference time decrease as the number of worker nodes increases.