Fake News Polarization for Sentiment Analysis

The popularity of the internet has increased the use of e-commerce websites and news channels. Fake news has been around for many years, and with the arrival of social media and modern-day news at its peak, easy access to e-platform and exponential growth of the knowledge available on social media networks has made it intricate to differentiate between right and wrong information, which has caused large effects on the offline society already. A crucial goal in improving the trustworthiness of data in online social networks is to spot fake news so the detection of spam news becomes important. For sentiment mining, the authors specialise in leveraging Facebook, Twitter, and Whatsapp, the most prominent microblogging platforms. They illustrate how to assemble a corpus automatically for sentiment analysis and opinion mining. They create a sentiment classifier using the corpus that can classify between fake, real, and neutral opinions in a document.

Sentiment Mining and Analysis over Text Corpora via Complex Deep Learning Naural Architectures

We analyze and compare five deep-learning neural architectures to manage the problem of irony and sarcasm detection for the Italian language. We briefly analyze the model architectures to choose the best compromise between performances and complexity. The obtained results show the effectiveness of such systems to handle the problem by achieving 93\% of F1-Score in the best case. As a case study, we also illustrate a possible embedding of the neural systems in a cloud computing infrastructure to exploit the computational advantage of using such an approach in tackling big data.

Tourist Sentiment Mining Based on Deep Learning

Mining the sentiment of the user on the internet via the context plays a significant role in uncovering the human emotion and in determining the exactness of the underlying emotion in the context. An increasingly enormous number of user-generated content (UGC) in social media and online travel platforms lead to development of data-driven sentiment analysis (SA), and most extant SA in the domain of tourism is conducted using document-based SA (DBSA). However, DBSA cannot be used to examine what specific aspects need to be improved or disclose the unknown dimensions that affect the overall sentiment like aspect-based SA (ABSA). ABSA requires accurate identification of the aspects and sentiment orientation in the UGC. In this book chapter, we illustrate the contribution of data mining based on deep learning in sentiment and emotion detection.

Sentiment Analysis on Twitter Airline Data

Sentiment analysis, also known as sentiment mining, is a submachine learning task where we want to determine the overall sentiment of a particular document. With machine learning and natural language processing (NLP), we can extract the information of a text and try to classify it as positive, neutral, or negative according to its polarity. In this project, We are trying to classify Twitter tweets into positive, negative, and neutral sentiments by building a model based on probabilities. Twitter is a blogging website where people can quickly and spontaneously share their feelings by sending tweets limited to 140 characters. Because of its use of Twitter, it is a perfect source of data to get the latest general opinion on anything.

Understanding Consumer Product Sentiments through Supervised Models on Cloud: Pre and Post COVID

While a lot of work is done on extracting sentiments and opinions in unstructured text, majority of it is focused on contextual sentiment mining and features that are more focused on sentiments. The team attempted to use contextual text analytics to identify product or service features that drives the sentiment of the user. This is done through application of cosine similarity and neural networks. Customers speak about product or service feature when it is important for the them. The second stage of the analysis is focused on supervised learning, that identifies key drivers of a product or service. It helps in deriving those elements which are subconsciously being evaluated by customers but not spoken. We also test the significant difference in views of people pre and post Covid in their reviews. We found that factors related to Covid have gone up by 30% but not statistically significant. Given the volume of data, the team has analyzed these on cloud to assess the cloud computing readiness for such analysis. Feedback around the post Covid topics helps us understand the issues that need to be addressed by restaurant industry.

How do network attributes impact information virality in social networks?

Purpose Social media platforms play a key role in information propagation and there is a need to study the same. This study aims to explore the impact of the number of close communities (represented by cliques), the size of these close communities and its impact on information virality. Design/methodology/approach This study identified 6,786 users from over 11 million tweets for analysis using sentiment mining and network science methods. Inferential analysis has also been established by introducing multiple regression analysis and path analysis. Findings Sentiments of content did not have a significant impact on the information virality. However, there exists a stagewise development relationship between communities of close friends, user reputation and information propagation through virality. Research limitations/implications This paper contributes to the theory by introducing a stagewise progression model for influencers to manage and develop their social networks. Originality/value There is a gap in the existing literature on the role of the number and size of cliques on information propagation and virality. This study attempts to address this gap.