Semiautomatic dictionary-based classification of environment tweets by topic

In the era of social media, the huge availability of digital data allows to develop several types of research in a wide range of fields. Such data is characterized by several advantages: reduced collection costs, short retrieval times and production of almost real-time outputs. At the same time, this data is unstructured and unclassified in terms of con-tent. This study aims to develop an efficient way to filter and analyze tweets by means of sentiment related to a specific topic.