Sentiment Analysis In Social Networks Research Articles

Methodology for Measuring Individual Affective Polarization Using Sentiment Analysis in Social Networks

Methodology for Measuring Individual Affective Polarization Using Sentiment Analysis in Social Networks

A Novel Approach of Deep Learning Using Sentiment Analysis in Python

This paper focused on Sentiment Analysis is the task of categorize the division of a given text. For instance, a text-based tweet can be categorize into either "positive", "negative", or "neutral". Given the text and accompanying labels, a model can be trained to predict the correct sentiment. Sentiment Analysis techniques can be categorized into Deep Learning approaches, lexicon-based approaches, and even hybrid methods. Some subcategories of research in sentiment analysis include: multimodal sentiment analysis, aspect-based sentiment analysis, fine-grained opinion analysis, language specific sentiment analysis. Sentiment analysis (SA) is a process of extensive exploration of data stored on the Web to identify and categorize the views expressed in a part of the text. The intended outcome of this process is to assess the author attitude toward a particular topic, movie, product, etc. The result is positive, negative, or neutral These study illustrated different techniques in SA approach for extracting and analytics sentiments associated with the polarity of positive, negative, or neutral on the topic selected. Social networks SA can be a useful source of information and data. SA acquires important in many areas of business, politics, and thought.. The main aim of this study is to provide full concept about SA techniques and its classification and methods used it. Also, we give a brief overview of big data techniques and its relation and use in SA field. Because the recent period has witnessed a remarkable development in the use of Big Data (Hadoop) in the process collection of data and reviews from social networks for analysis.

Social Network Sentiment Analysis Using Hybrid Deep Learning Models

The exponential growth in information on the Internet, particularly within social networks, highlights the importance of sentiment and opinion analysis. The intrinsic characteristics of the Spanish language coupled with the short length and lack of context of messages on social media pose a challenge for sentiment analysis in social networks. In this study, we present a hybrid deep learning model combining convolutional and long short-term memory layers to detect polarity levels in Twitter for the Spanish language. Our model significantly improved the accuracy of existing approaches by up to 20%, achieving accuracies of around 76% for three polarities (positive, negative, neutral) and 91% for two polarities (positive, negative).

Contribution to the Moroccan Darija sentiment analysis in social networks

Contribution to the Moroccan Darija sentiment analysis in social networks

Sentiment analysis in social networks of health institutions

Twitter, a communication platform that creates a social impact; it conveys the messages of non-profit organizations to the masses and the emotions of the masses to non-profit organizations. This research; It aims to examine Twitter posts about health-related non-profit organizations, to determine the emotional states about these institutions on social media and to measure these feelings. 
 Sentiment analysis about WHO, ILO, IOM, UNICEF, FAO, Red Cross, UNDP and UNHCR were carried out using the R program. The tweets used in sentiment analysis were collected by approval of Twitter API. During the study, a total of 310,341 tweets were collected in three periods, November 2019, May 2020 and October 2020. Tweets are classified according to 10 different emotions. One of the main findings of the study is that “positive”, “trust” and “anticipation” feelings are at the top of the tweets shared about these institutions under normal conditions and crisis conditions. Sentiment consistency was tested with Friedman test for each institution after emotional analysis was performed in all institutions (p

Using Social Network Sentiment Analysis and Genetic Algorithm to Improve the Stock Prediction Accuracy of the Deep Learning-Based Approach

Traditionally, most investment tools used to predict stocks are based on quantitative variables, such as finance and capital flow. With the widespread impact of the Internet, investors and investment institutions designing investment strategies are also referring to online comments and discussions. However, multiple information sources, along with uncertainties accompanying international political and economic events and the recent pandemic, have left investors concerned about information interpretation approaches that could aid investment decision-making. To this end, this study proposes a method that combines social media sentiment, genetic algorithm (GA), and deep learning to predict changes in stock prices. First, it employs a hybrid genetic algorithm (HGA) combined with machine learning to identify chip-based indicators closely related to fluctuations in stock prices and then uses them as input for long short-term memory (LSTM) to establish a prediction model. Next, this study proposes five sentiment variables to analyze PTT social media on TSMC’s stock price and performs a grey relational analysis (GRA) to identify the sentiment variables most closely related to stock price fluctuations. The sentiment variables are then combined with the selected chip-based indicators as input to build the LSTM prediction model. To improve the efficiency of the LSTM analysis, this study applies the Taguchi method to optimize the hyper-parameters. The results show that the proposed method of using HGA-screened chip-based variables and social media sentiment variables as input to establish an LSTM prediction model can effectively improve the prediction accuracy of stock price fluctuations.

A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques

Sentiment Analysis (SA) of text reviews is an emerging concern in Natural Language Processing (NLP). It is a broadly active method for analyzing and extracting opinions from text using individual or ensemble learning techniques. This field has unquestionable potential in the digital world and social media platforms. Therefore, we present a systematic survey that organizes and describes the current scenario of the SA and provides a structured overview of proposed approaches from traditional to advance. This work also discusses the SA-related challenges, feature engineering techniques, benchmark datasets, popular publication platforms, and best algorithms to advance the automatic SA. Furthermore, a comparative study has been conducted to assess the performance of bagging and boosting-based ensemble techniques for social network SA. Bagging and Boosting are two major approaches of ensemble learning that contain various ensemble algorithms to classify sentiment polarity. Recent studies recommend that ensemble learning techniques have the potential of applicability for sentiment classification. This analytical study examines the bagging and boosting-based ensemble techniques on four benchmark datasets to provide extensive knowledge regarding ensemble techniques for SA. The efficiency and accuracy of these techniques have been measured in terms of TPR, FPR, Weighted F-Score, Weighted Precision, Weighted Recall, Accuracy, ROC-AUC curve, and Run-Time. Moreover, comparative results reveal that bagging-based ensemble techniques outperformed boosting-based techniques for text classification. This extensive review aims to present benchmark information regarding social network SA that will be helpful for future research in this field.

Hybrid LSTM and GRU for Cryptocurrency Price Forecasting Based on Social Network Sentiment Analysis Using FinBERT

Hybrid LSTM and GRU for Cryptocurrency Price Forecasting Based on Social Network Sentiment Analysis Using FinBERT

Application of the Deep Pretrained Language Model Processing Method in Social Network Sentiment Analysis

In social network, users can manage their social network and social identity, publish information on various topics, and obtain information published by other users through friend relationship. The resulting large amount of text data attract more and more scholars to study it. Text sentiment analysis has become a hot spot in social network data analysis and has important application value in academic field, social field, and business field. Based on the idea of pre-training, this paper improves the random word masking algorithm of deep pretraining task in the BERT (Bidirectional Encoder Representation) model to improve the efficiency and stability of model pretraining. Second, a new pretraining task of original sentence judgment is designed to enable the model to measure the degree of sentence smoothness, so that the BERT model can better understand the semantics of context. By referring to the idea of attention mechanism, a deep learning framework with attention weight added into gated convolution is constructed and the special attention weight method is adopted to enhance semantic information. Second, gating convolution and attention mechanism are combined to model aspect-related semantic information and text complete semantic information. Finally, classify the emotion classifier layer of social network, use Softmax function to complete negative, positive, and neutral multiple classifications and calculate the result of emotion classification. By applying the optimized convolutional neural network cyclic optimization network to single task and multitask in practice, the feasibility of applying the optimized convolutional neural network and cyclic neural network to social network sentiment analysis is verified.

Deep Learning-Based Natural Language Processing Methods for Sentiment Analysis in Social Networks

This paper presents an in-depth study of the sentiment of social network communication through a deep learning-based natural language processing approach and designs a corresponding model to be applied in the actual social process. Specifically, the network can dynamically select the most important word in the current state according to the information available and achieve the accurate recognition of the dynamically changing important content in a sentence. Based on this, the semantic understanding of the whole sentence is achieved through a continuous cycle of the process. In addition, considering that the semantic representation of natural language is highly dependent on contextual information, the lack of contextual information will lead to the ambiguity and inaccuracy of semantic representation. In this paper, we study the sentiment analysis algorithms in social networks at two levels, unimodal and multimodal, and construct a text sentiment analysis model and a picture-text multimodal sentiment analysis model in social networks, respectively. By comparing the experiments with the existing models on several datasets, the accuracy of the two models exceeded the benchmark models by 4.45% and 5.2%, respectively, which verified the effectiveness of the two models. The feasibility of applying the optimized convolutional neural network recurrent optimization network to social network sentiment analysis is verified by practically applying the optimized convolutional neural network recurrent optimization network to single task and multitask and comparing other existing deep learning classifiers.

Sentiment Analysis in Social Networks Using Social Spider Optimization Algorithm

Sentiment Analysis in Social Networks Using Social Spider Optimization Algorithm

Building a fuzzy sentiment dimension for multidimensional analysis in social networks

The continuous growth of social networks has made them one of the main information sources for researchers and companies, but at the same time, their pre-processing and analysis represent a great challenge. In this work, we create a Fuzzy Sentiment Dimension from textual data from social networks to allow sentiment analysis jointly with standard dimensions in a multidimensional model, and make easier and more flexible sentiment analysis in social networks. In particular, we use technologies such as fuzzy logic and multidimensional analysis, together with unsupervised tools for sentiment analysis. The use of unsupervised tools for sentiment analysis also allows both previously labeled and unlabeled documents to be analyzed. The performance results of the experimentation demonstrate the feasibility of the proposal.

Modified fuzzy sentiment analysis approach based on user ranking suitable for online social networks

The rapidly increasing of sentiment analysis in social networks has lead business owners and decision makers to value opinion leaders who can influence people's impressions concerning certain business or commodity. Nevertheless, decision makers are being misled by inaccurate results due to the ignorance of perspectivism. Considering perspectivism, while computing text polarity, can help machines to reflect the human perceived sentiment within the content. This emphasises the need for integrating social behaviour (user's influence factor) with sentiment analysis (text polarity scores), providing a more pragmatic portrayal of how the writer's audience comprehend the message. In this study, a new model is proposed to intensify sentiment analysis process on Twitter. In the achievement of such, social network analysis is done using UCINET tool followed by artificial neural networks for ranking users. For sentiment classification, a hybrid approach is presented, where lexicon-based technique is combined with a fuzzy classification technique to handle language vagueness as well as for an inclusive analysis of tweets into seven classes; for the purpose of enhancing final results. The proposed model is practiced on data collected from Twitter. Results show a significant enhancement in tweets polarity scores represent more realistic sentiments.

STCS Lexicon: Spectral-Clustering-Based Topic-Specific Chinese Sentiment Lexicon Construction for Social Networks

A sentiment lexicon is an important foundation for social network sentiment analysis. However, in social networks, the sentiments of words vary with their topics of use, which leads to a problem in the construction of a sentiment lexicon. We propose a method for constructing a topic-specific sentiment lexicon, which comprises three following models: first, we propose a filtering text model, namely, FT model, to calculate the text influence value and obtain topic-specific hot comments as a preprocessing data set; second, in our proposed constructing sentiment relationship graph model, namely, CRM model, three factors, i.e., the base sentiment similarity, topic sentiment similarity, and synonym sentiment similarity between each pair of sentiment words, are proposed and calculated in our data set, and then we can obtain the factor of final sentiment similarity by adding the three values in proportion; and finally, we propose a spectral clustering model, namely, SC model, to cluster the sentiment words on the basis of a sentiment relationship graph for obtaining the topic-specific sentiment lexicon, namely, STCS lexicon. Experiments show that our method is simple, flexible, and efficient. It can solve the problem of topic-related sentiment words and, thus, improve the accuracy of the sentiment lexicon.

A real-time social network-based knowledge discovery system for decision making

ABSTRACTThe increasing amount of data in social networks has complicated data processing and interpretation. Therefore, intelligent decision-support mechanisms that have the ability to automatically extract meaning from data and interpret the opinions of people in real time have become inevitable. In this study, an intelligent multilingual decision support system was implemented, and a new algorithm that employs text mining and sentiment analysis techniques was developed to automatically interpret the opinions of social network users about the places they plan to visit. The system can be used as a baseline for sentiment analysis in social networks and can be adapted to build new systems. In this study, we set our main focus on Turkish language and show the applicability of our approach for other languages through the experiments for English language. The dataset required for the implementation of text mining techniques was created based on the venue recommendations shared on Foursquare social media platform. As a result, a contribution was made to the way the social network users make decisions without reading thousands of recommendations. Our results show that the developed system achieves classification accuracy of 84.49% for Turkish and 95% for English. Finally, the most liked or disliked foods/beverages are correctly identified for 107 out of 128 venues.

Positive and Negative Link Prediction Algorithm Based on Sentiment Analysis in Large Social Networks

Signed network analysis being one of the greatest disruptive innovations within the last decade has assembled a vast amount of attention of the citizenry. The positions of the users of the signed networks are used by several societies in the world to see the mentality of the users, the current movement of the grocery store and many more things. But even so, in that location is a latent potential of social nets. Ace of the facial expressions that, we were able to determine was about seeing the relationship between the users (i.e., especially, the negative (i.e., −Ve) link in social networks) on the signed network using the stakes that the users work and the reaction of the other users towards it. The anticipation of a negative link (i.e., −Ve) can be applied in the information security field, to observe the aberrations in the largest social networks and further discover the malicious nodes in the larger social network; say, if two nodes are doing things together even though in that respect is no intercourse between them. It can likewise be utilized in improving the recommendation system in social networks as if there is some probability between the two the nodes of being an enemy or disliking each other then we can slay them from each other’s recommendation list or could assign a lesser weight to them in a recommended technique. To accomplish all this relationship between the nodes we first need to determine whether the user is posting posts with positive emotion (like happy, excited, etc.) or negative emotion (like angry, sad, and so on), and then that we can further examine the learning ability of the user and utilize it to recommend the people who we have previously separated with the similar personality. For that we have applied the sentiment analysis in social networks, which splits up the users into five simple categories: Highly Positive (i.e., Highly +Ve), Positive (i.e., +Ve), Neutral, Negative (i.e., −Ve) and Highly Negative (i.e., Highly −Ve).

Big Data analytics and Computational Intelligence for Cyber–Physical Systems: Recent trends and state of the art applications

Big data is fuelling the digital revolution in an increasingly knowledge driven and connected society by offering big data analytics and computational intelligence based solutions to reduce the complexity and cognitive burden on accessing and processing large volumes of data. In this paper, we discuss the importance of big data analytics and computational intelligence techniques applied to data produced from the myriad of pervasively connected machines and personalized devices offering embedded and distributed information processing capabilities. We provide a comprehensive survey of computational intelligence techniques appropriate for the effective processing and analysis of big data. We discuss a number of exemplar application areas that generate big data and can hence benefit from its effective processing. State of the art research and novel applications in health-care, intelligent transportation and social network sentiment analysis, are presented and discussed in the context of Big data, Cyber–Physical Systems (CPS), and Computational Intelligence (CI). We present a data modelling methodology, which introduces a novel biologically inspired universal generative modelling approach called Hierarchical Spatial–Temporal State Machine (HSTSM). The HSTSM modelling approach incorporates a number of soft computing techniques such as: deep belief networks, auto-encoders, agglomerative hierarchical clustering and temporal sequence processing, in order to address the computational challenges arising from analysing and processing large volumes of diverse data to provide an effective big data analytics tool for diverse application areas. A conceptual cyber–physical architecture, which can accommodate and benefit from the proposed methodology, is further presented.

Approval network: a novel approach for sentiment analysis in social networks

The data-centric impetus and the development of online social networks has led to a significant amount of research that is nowadays more flexible in demonstrating several sociological hypotheses, such as the sentiment influence and transfer among users. Most of the works regarding sentiment classification usually consider text as unique source of information, do not taking into account that social networks are actually networked environments. To overcome this limitation, two main sociological theories should be accounted for addressing any sentiment analysis tasks: homophily and constructuralism. In this paper, we propose Approval Network as a novel graph representation to jointly model homophily and constructuralism, which is intended to better represent the contagion on social networks. To show the potentiality of the proposed representation, two novel sentiment analysis models have been proposed. The first one, related to user-level polarity classification, is approached by presenting a semi-supervised framework grounded on a Markov-based probabilistic model. The second task, aimed at simultaneously extracting aspects and sentiment at message level, is addressed by proposing a novel fully unsupervised generative model. The experimental results show that the proposes sentiment analysis models grounded on Approval Networks are able to outperform not only the traditional models where the relationships are disregarded, but also those computational approaches based on traditional friendship connections.

Generalization of Information Spreading Forensics via Sequential Dependent Snapshots

Learning the characteristics of information spreading in networks is crucial in communication studies, social network sentiment analysis and epidemic investigation. Previous work on information spreading has been focused on the information source detection using either a single observation, or multiple but "independent" observations of the underlying network while assuming information spreads at a "uniform spreading rate" . In this paper, we conduct the first theoretical and experimental study on information spreading forensics, and propose a framework for estimating information spreading rates, information source start time and location of information source by utilizing "multiple sequential and dependent snapshots" where information can spread at different rates. We prove that our estimation framework generalizes the rumor centrality [1], and we allow heterogeneous information spreading rates on different branches in d -regular tree networks. We further show that our framework can provide highly accurate estimates for the information spreading rates on different branches, the source start time, and more accurate estimate for the location of information source than rumor centrality and Jordan center in both synthetic networks and real-world networks (i.e., Twitter).

Emotions ontology for collaborative modelling and learning of emotional responses

Emotions-aware applications are getting a lot of attention as a way to improve the user experience, and also thanks to increasingly affordable Brain–Computer Interfaces (BCI). Thus, projects collecting emotion-related data are proliferating, like social networks sentiment analysis or tracking students’ engagement to reduce Massive Online Open Courses (MOOCs) drop out rates. All them require a common way to represent emotions so it can be more easily integrated, shared and reused by applications improving user experience. Due to the complexity of this data, our proposal is to use rich semantic models based on ontology. EmotionsOnto is a generic ontology for describing emotions and their detection and expression systems taking contextual and multimodal elements into account. The ontology has been applied in the context of EmoCS, a project that collaboratively collects emotion common sense and models it using the EmotionsOnto and other ontologies. Currently, emotion input is provided manually by users. However, experiments are being conduced to automatically measure users’s emotional states using Brain–Computer Interfaces.