Twitter Sentiment Analysis: Application for Classifying Tweets with Video Games as Keywords

The growth of microblogging services has expanded exponentially in recent years for mining user opinions. Sentiment analysis was applied to classify Twitter posts with video game titles as keywords. An analysis of the blog history, words and sentiments associated with the blog can help reveal whether the particular game is ‘violent’ and stress inducing or ‘non-violent’ and benign. An application was developed to collect and clean data. Naïve Bayes algorithm was applied to the cleaned data to determine the polarity of the words on the data to come to a conclusion whether, based on the words of the tweet, the particular game could be classified as ‘violent’ or ‘non-violent’. The results of the algorithm are analysed for accuracy, precision and recall. Deep learning models are discussed for use in future to improve accuracy

Investigating Predictive Power of Stock Micro Blog Sentiment in Forecasting Future Stock Price Directional Movement

This study attempts to discover and evaluate the predictive power of stock micro blog sentiment on future stock price directional movements. We construct a set of robust models based on sentiment analysis and data mining algorithms. Using 72,221 micro blog postings for 1909 stock tickers and 3874 distinct authors, our study reveals not only that stock micro blog sentiments do have predictive power for simple and market-adjusted returns respectively, but also that this predictive accuracy is consistent with the underreaction hypothesis observed in behavioral finance. We establish that stock micro blog with its succinctness, high volume and real-time features do have predictive power over future stock price movements. Furthermore, this study provides support for the model of irrational investor sentiment, recommends a supplementary investing approach using user-generated content and validates an instrument that may contribute to the monetization schemes for Virtual Investing Communities

MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter

To alleviate the impact of fake news on our society, predicting the popularity of fake news posts on social media is a crucial problem worthy of study. However, most related studies on fake news emphasize detection only. In this paper, we focus on the issue of fake news influence prediction, i.e., inferring how popular a fake news post might become on social platforms. To achieve our goal, we propose a comprehensive framework, MUFFLE, which captures multi-modal dynamics by encoding the representation of news-related social networks, user characteristics, and content in text. The attention mechanism developed in the model can provide explainability for social or psychological analysis. To examine the effectiveness of MUFFLE, we conducted extensive experiments on real-world datasets. The experimental results show that our proposed method outperforms both state-of-the-art methods of popularity prediction and machine-based baselines in top-k NDCG and hit rate. Through the experiments, we also analyze the feature importance for predicting fake news influence via the explainability provided by MUFFLE

Main contribution of iconic attractions towards increasing popularity of tourism destinations: an analysis of twitter posts and locations

The social media platforms, due to their universal and comfortable interface, have become the real enablers of a microblogging services. Moreover, with the evolution of online reviews, consumers feel comfortable to express their opinions and share their personal experiences not only about the brands, but also about the travel destinations. Henceforth, social networks such as, Twitter, became important source of information. In this study, author analyzes 4,000 Twitter posts about 2 popular and 2 less popular locations and associated derived sentiments. The study demonstrates that there is a certain difference in perception of locations with a different popularity rank. In terms of information exposure, more popular locations tend to have a higher message diffusion activity, with most of them being of neutral polarity. Additionally, results showed that negative affection is observed more for less popular locations, providing valuable insight for Destination Marketing Organizations. In addition, for both groups, role of followers’ base was ineffective, demonstrating that topic of message sentiment and diffusion are key in tourism domain. Thus, from a methodological point of view, the main contribution of this research is the usage of random and unstructured data in Twitter to the measurement of the perception of the potential visitors of tourist attractions based on the sentiment analysis of posts associated to them. From theoretical point of view, using the sentiment orientation, the study relates to the user exposure and affection of the iconic attractions by the perceived difference in their popularity in accordance with external destination ranking.As redes sociais, devido ao seu interface universal e confortável, tornaram-se reais facilitadores de serviços de microblogging. Por conseguinte, a evolução dos reviews on-line, conferiu aos consumidores maior conforto para expressar as suas opiniões e partilhar as suas experiências pessoais, não apenas sobre as marcas, mas também sobre os seus destinos de viagem. As redes sociais, como o Twitter, tornaram-se importantes fontes de informação. Neste estudo, o autor analisa os sentimentos derivados de 4.000 publicações do Twitter acerca de 2 locais turísticos mais populares e 2 menos populares. O estudo demonstra que há uma certa diferença na percepção dos locais em função do seu grau de popularidade. Em termos de exposição, os locais mais populares tendem a ter uma maior atividade de difusão nas suas mensagens, sendo a maioria delas de polaridade neutra. Adicionalmente, os resultados mostraram que o sentimento negativo é mais partilhado em locais menos populares, fornecendo informações valiosas para Organizações de Marketing. Não obstante, para ambos os grupos, a dimenção da base de seguidores foi irrelevante, demonstrando que o tema da mensagem sentimento e difusão são fundamentais no domínio do turismo. A nível metodológico, o principal contributo desta pesquisa é a análise do sentimento de dados aleatórios e desestruturados do Twitter para a medição da percepção acerca de atracções turísticas com base na. Do ponto de vista teórico, o estudo relaciona-se com a exposição do usuário e o sentimento das atrações icônicas pela diferença percebida na sua popularidade de acordo com um ranking de destinos externo