This paper explores the possibility that the statistical methodology ANOVA (ANalysis Of VAriance) can detect interesting relationships between the characteristics of tweets and the average rating of a movie. The main objective of ANOVA test is to look for statistically significant differences in a variable from the information provided by one or more independent variables. In this case, the selected response variable is the average rating of each movie, whose variability is studied with the different values of the movie or Twitter data variables. Thus, if a significative difference is found in an ANOVA, the movie or Twitter variable analyzed could affect the mean rating of the movie. There are multiple websites where users can find a database of movies and rate the ones they watch. One of the most famous is IMDb, an acronym for Internet Movie Data Base, with millions of titles and user records. But the movie experience doesn’t start with the rating, or even the viewing, but with the conversation about the movie even before the release. This study focuses on studying the reliability of the relationship between the conversation around movies on Twitter and the rating the movie gets a few weeks after its premiere. This paper explores the possibility of using ANOVA as a methodology to search for relationships between average rating and usage, conversation, engagement and amplification rates of official movie hashtags. Considering the 36 movies that were premiered in February 2022 on four different dates, all tweets that were posted the week before, the week after and two weeks after the release of each film were extracted from the Twitter API. The total sum was 389,649 tweets, all posted between 28th January 2022 and 10th March 2022. This extraction allowed us to obtain some characteristics of each tweet, such as tweet count, quotes, replies, retweets, likes and sentiment analysis of the content.s An interesting conclusion of this study is that the ANOVA has detected relationships such as the one between the tweet sentiment analysis and the average rating of movies on iMDB. Since this work has analyzed each week separately, in different ANOVAs, this methodology has shown that the tweets posted in the week before the movie release are especially related to the average rating. This work paves the way for the use of ANOVA in trend analysis of movie hashtags over a longer time and for a deeper and broader study of sentiment analysis of the digital conversation produced on Twitter about movie premieres.