General Sentiment Decomposition: Climate Change topic & Twitter.com users

Word of Mouth political and marketing importance is growing day by day. These phenomena can be directly observed in everyday life, e.g.: the rise of influencers and social media managers. If more people talk about a specific product, then more people are encouraged to buy it and vice versa. This effect is amplified proportionally to how high the consideration or close the relationship between the potential customer and the reviewer is. Furthermore, considering the negative reporting bias, it is easy to understand how customer satisfaction is of absolute interest for a company (or for a politician). After analyzing the impact of Word of Mouth on earnings and the related psychological aspects, we propose an algorithm to extract the sentiment from a natural language text corpus. The combined approach of Neural Networks, characterized by high predictive power but at the cost of harder interpretation, with more straightforward and informative models, allows not only to predict how much a sentence is positive (negative) but also to quantify a sentiment with a numeric value. The assessment of an objective quantity improves the interpretation of the results in many fields. For example, it is possible to identify specific critical sectors that require intervention to improve the offered services, to find the strengths of the company (useful for advertising campaigns), and, if time information is present, to analyze trends on macro/micro topics. To support further decision-making, we apply this method to Twitter's data, analyzing the sentiment of people who discuss environmental issues. In this way, we identify the aspects that are perceived as critical by the people w.r.t. the "feedback" they publish on the web and quantify how happy (or not) they are about a particular climate change-related problem.