Targeted Twitter Sentiment Analysis for Brands Using Supervised Feature Engineering and the Dynamic Architecture for Artificial Neural Networks

Classification of Tweets Into Facts and Opinions Using Recurrent Neural Networks

In the last few years, the growth rate of the number of people who are active on Twitter has been consistently spiking. In India, even the government agencies have started using Twitter accounts as they feel that they can get connected to a greater number of people in a short span of time. Apart from the social media platforms, there are an enormous number of blogging applications that have popped up providing another platform for the people to share their views. With all this, the authenticity of the content that is being generated is going for a toss. On that note, the authors have the task in hand of differentiating the genuineness of the content. In this process, they have worked upon various techniques that would maximize the authenticity of the content and propose a long short-term memory (LSTM) model that will make a distinction between the tweets posted on the Twitter platform. The model in combination with the manually engineered features and the bag of words model is able to classify the tweets efficiently.

Artificial Intelligence and Sentiment Analysis: A Review in Competitive Research

As part of a business strategy, effective competitive research helps businesses outperform their competitors and attract loyal consumers. To perform competitive research, sentiment analysis may be used to assess interest in certain themes, uncover market conditions, and study competitors. Artificial intelligence (AI) has improved the performance of multiple areas, particularly sentiment analysis. Using AI, sentiment analysis is the process of recognizing emotions expressed in text. AI comprehends the tone of a statement, as opposed to merely recognizing whether particular words within a group of text have a negative or positive connotation. This article reviews papers (2012–2022) that discuss how competitive market research identifies and compares major market measurements that help distinguish the services and goods of the competitors. AI-powered sentiment analysis can be used to learn what the competitors’ customers think of them across all aspects of the businesses.

Artificial intelligence in E-Commerce: a bibliometric study and literature review

This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes guidelines on how information systems (IS) research could contribute to this research stream. To this end, the innovative approach of combining bibliometric analysis with an extensive literature review was used. Bibliometric data from 4335 documents were analysed, and 229 articles published in leading IS journals were reviewed. The bibliometric analysis revealed that research on AI in e-commerce focuses primarily on recommender systems. Sentiment analysis, trust, personalisation, and optimisation were identified as the core research themes. It also places China-based institutions as leaders in this researcher area. Also, most research papers on AI in e-commerce were published in computer science, AI, business, and management outlets. The literature review reveals the main research topics, styles and themes that have been of interest to IS scholars. Proposals for future research are made based on these findings. This paper presents the first study that attempts to synthesise research on AI in e-commerce. For researchers, it contributes ideas to the way forward in this research area. To practitioners, it provides an organised source of information on how AI can support their e-commerce endeavours.

Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics

The Coronavirus pandemic has created complex challenges and adverse circumstances. This research identifies public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential public sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research focuses on emotional consequences - the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, time-aligned to the COVID-19 reopening debate, to identify dominant sentiment trends associated with the push to reopen the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios - an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of textual data analytics, including textual data visualization and statistical validation, that tweets data from American Twitter users shows more positive sentiment support, than negative, for reopening the US economy. This research develops a novel sentiment polarity based public sentiment scenarios (PSS) framework, which will remain useful for future crises analysis, well beyond COVID-19. With additional validation, this research stream could present valuable time sensitive opportunities for state governments, the federal government, corporations and societal leaders to guide local and regional communities, and the nation into a successful new normal future.

The State-of-the-Art in Twitter Sentiment Analysis

Twitter has emerged as a major social media platform and generated great interest from sentiment analysis researchers. Despite this attention, state-of-the-art Twitter sentiment analysis approaches perform relatively poorly with reported classification accuracies often below 70%, adversely impacting applications of the derived sentiment information. In this research, we investigate the unique challenges presented by Twitter sentiment analysis and review the literature to determine how the devised approaches have addressed these challenges. To assess the state-of-the-art in Twitter sentiment analysis, we conduct a benchmark evaluation of 28 top academic and commercial systems in tweet sentiment classification across five distinctive data sets. We perform an error analysis to uncover the causes of commonly occurring classification errors. To further the evaluation, we apply select systems in an event detection case study. Finally, we summarize the key trends and takeaways from the review and benchmark evaluation and provide suggestions to guide the design of the next generation of approaches.