Analysing discussions in social networks using group decision making methods and sentiment analysis

A method to detect influencers in social networks based on the combination of amplification factors and content creation

A social network is one of the efficient tools for information propagation. The content is the bridge between the product and its customers. Evaluating the user's content creation is a valuable feature to improve information spreading on the social network. This paper proposes a method for extracting brand value with influencers by combining the user's amplification and content creation in influencer marketing. The amplification factors are studied based on the propagation of the posts on the social network in a duration time. Those factors are more valuable than before when using influencer marketing at a determined time. Moreover, the content creation score is also studied to measure content creation based on the passion point with a brand and its quality. The amplification factors and content creation score are combined to analyze posts' interest in detecting the emerging influent users for a product in the influencer marketing campaign. Using the amplification factors, the passion points, and the content creation score, a system to manage the influencer marketing on Facebook has been constructed and tested in the real-world campaign. The experimental results show that the proposed method's influencers bring the conversion rate's efficiency and revenue in the influencer marketing campaign.

Sentiment Analysis of Image with Text Caption using Deep Learning Techniques

People are actively expressing their views and opinions via the use of visual pictures and text captions on social media platforms, rather than just publishing them in plain text as a consequence of technical improvements in this field. With the advent of visual media such as images, videos, and GIFs, research on the subject of sentiment analysis has expanded to encompass the study of social interaction and opinion prediction via the use of visuals. Researchers have focused their efforts on understanding social interaction and opinion prediction via the use of images, such as photographs, films, and animated GIFs (graphics interchange formats). The results of various individual studies have resulted in important advancements being achieved in the disciplines of text sentiment analysis and image sentiment analysis. It is recommended that future studies investigate the combination of picture sentiment analysis and text captions in more depth, and further research is necessary for this field. An intermodal analysis technique known as deep learning-based intermodal (DLBI) analysis is discussed in this suggested study, which may be used to show the link between words and pictures in a variety of scenarios. It is feasible to gather opinion information in numerical vector form by using the VGG network. Afterward, the information is transformed into a mapping procedure. It is necessary to predict future views based on the information vectors that have been obtained thus far, and this is accomplished through the use of active deep learning. A series of simulation tests are being conducted to put the proposed mode of operation to the test. When we look at the findings of this research, it is possible to infer that the model outperforms and delivers a better solution with more accuracy and precision, as well as reduced latency and an error rate, when compared to the alternative model (the choice).

Intermodal Sentiment Analysis for Images with Text Captions Using the VGGNET Technique

More individuals actively express their opinions and attitudes in social media through advanced improvements such as visual content and text captions. Sentiment analysis for visuals such as images, video, and GIFs has become an emerging research trend in understanding social involvement and opinion prediction. Numerous individual researchers have obtained good progress in outcomes for text sentiment analysis and image sentiment analysis. The combination of image sentiment analysis with text caption analysis needs more research. This article presents a VGG Network-based Intermodal Sentiment Analysis Model (VGGNET-ISAM) for transferring the connection between texts to images. A mapping process is developed using the VGG Network for gathering the opinion information as numerical vectors. The Active Deep Learning (ADL) classifier is used for opinion prediction from the obtained information vectors. Simulation experiments are carried out to evaluate the proposed approach. The findings show that the model outperforms and gives better solutions with high accuracy, precision with low delay, and low error rate.

A Hybrid Multilingual Fuzzy-Based Approach to the Sentiment Analysis Problem Using SentiWordNet

Sentiment Analysis or in particular social network analysis (SNA) is a new research area which is increased explosively. This domain has become a very active research issue in data mining and natural language processing. Sentiment analysis (opinion mining) consists in analyzing and extracting emotions, opinions or attitudes from product’s reviews, movie's reviews, etc., and classify them into classes such as positive, negative and neutral, or extract the degree of importance (polarity). In this paper, we propose a new hybrid approach for classifying tweets into classes based on fuzzy logic and a lexicon based approach using SentiWordnet. Our approach consists in classifying tweets according to three classes: positive, negative or neutral, using SentiWordNet and the fuzzy logic with its three important steps: Fuzzification, Rule Inference/aggregation, and Defuzzification. The dataset of tweets to classify and the result of the classification are stored in the Hadoop Distributed File System (HDFS), and we use the Hadoop MapReduce for the application of our proposal.

Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †

Influencer marketing is a modern method that uses influential users to approach goal customers easily and quickly. An online social network is a useful platform to detect the most effective influencer for a brand. Thus, we have an issue: how can we extract user data to determine an influencer? In this paper, a model for representing a social network based on users, tags, and the relationships among them, called the SNet model, is presented. A graph-based approach for computing the impact of users and the speed of information propagation, and measuring the favorite brand of a user and sharing the similar brand characteristics, called a passion point, is proposed. Therefore, we consider two main influential measures, including the extent of the influence on other people by the relationships between users and the concern to user’s tags, and the tag propagation through social pulse on the social network. Based on these, the problem of determining the influencer of a specific brand on a social network is solved. The results of this method are used to run the influencer marketing strategy in practice and have obtained positive results.

Social Profiling: A Review, Taxonomy, and Challenges

Social media has taken an important place in the routine life of people. Every single second, users from all over the world are sharing interests, emotions, and other useful information that leads to the generation of huge volumes of user-generated data. Profiling users by extracting attribute information from social media data has been gaining importance with the increasing user-generated content over social media platforms. Meeting the user's satisfaction level for information collection is becoming more challenging and difficult. This is because of too much noise generated, which affects the process of information collection due to explosively increasing online data. Social profiling is an emerging approach to overcome the challenges faced in meeting user's demands by introducing the concept of personalized search while keeping in consideration user profiles generated using social network data. This study reviews and classifies research inferring users social profile attributes from social media data as individual and group profiling. The existing techniques along with utilized data sources, the limitations, and challenges are highlighted. The prominent approaches adopted include Machine Learning, Ontology, and Fuzzy logic. Social media data from Twitter and Facebook have been used by most of the studies to infer the social attributes of users. The studies show that user social attributes, including age, gender, home location, wellness, emotion, opinion, relation, influence, and so on, still need to be explored. This review gives researchers insights of the current state of literature and challenges for inferring user profile attributes using social media data.