Towards a Sentiment Analysis of Tweets from Online Newspapers Regarding the Coronavirus Pandemic

In the last year, both offline and online news have had the Coronavirus pandemic as their subject, especially since social networking such as Twitter has significantly increased the news regarding Covid-19. The objectives of the project are: the analysis of news regarding the Coronavirus pandemic was extracted from the Twitter profile of ANSA, a well-known Italian news agency, and the analysis of sentiment and the number of likes for each news extracted The sentiment analysis has been carried out using the MAL lexicon (Morphologically Affective Lexicon), where the tweet is split into words and each paola is associated with a score. Positive (with a score greater than zero), negative (with a score less than zero) and neutral (with a score equal to zero) news were identified. As a result, it emerges that sentiment changes day by day, so it is necessary to use sentiment indicators called indices, but only the positive sentiment index is taken into consideration as the negative one is complementary and the neutral one is almost zero. The positive index is then related to some parameters extrapolated from the Civil Protection site: the number of cases, the number of deaths, and the entry into intensive care. Furthermore, in addition to the parameters listed above, the positivity index is related to the days on which the Prime Minister's Decree (DPCM) was signed. The last relationship analyzed is that between the average number of likes and the number of deaths. The results of the research show that the sentiment of the news from the ANSA Agency contains 62.3% of positive news, 37.3% of negative news, and only 0.3% of neutral news. Furthermore, sentiment is not influenced by the daily parameters: the number of cases, number of deaths, entry into intensive care units, and DPCMs. But there is a relationship between the average of like and the number of deaths.