The use of social networks has allowed us to enter into a new dimension in the estimation of investor sentiment. Indeed, they reach the same results as surveys’ while freeing themselves from the obvious constraints of the latter. Several studies have thus emerged, each with its own interesting conclusions. Siganos (2014) found by analysing Facebook data that the link between sentiment and the market was stronger for small caps, going in the direction of Baker’s hypothesis. Renault (2017), on the other hand, looked at intraday sentiment and finds that sentiment in the first half-hour of the session is useful for predicting performance in the last half hour of the session. Like Sprenger (2010) or Ranco (2015), it was chosen to use the social network Twitter to build the database. It is an interesting social network in that all users can interact with each other, even if they do not follow each other. This allows people with no relationship to discuss a common topic. As noted by Sprenger (2010), the format of Twitter means that it makes it possible to have discussions just like one might have in a trading room.All tweets are about S&amp;P500 companies (identified by the $ticker cashtag) and are extracted by connecting to the Twitter API via Python. The database is composed of 3 million tweets spanning from October 4, 2019 to May 26, 2021 (600 days). That corresponds to a number of 150,000 tweets per month. For comparison, this is more than Sprenger (2010) or even Ranco (2015) who had each constituted databases of 250,000 tweets (42,000 per month) and 1.5M (100,000 per month). Each of the tweets contains the following information: date of creation of the tweet, author id, number of retweets and quotes, number of followers of the author, and the text of the tweet.For scoring each tweet, it was first necessary to standardize the text in the same way as Thomas Renault (2017) because it will be used the same method as him to analyse them. Thus, each tweet was first put in lower case. Positive emojis (negative emojis) were replaced by the word «emojipos» (»emojineg»), numbers by the word «numbertag», cashtag by «cashtag», mentions by «usertag» and URL links by «linktag». The only stopwords that have been removed are the words «a», «an» and «the». Finally, negations with words such as «not», «no», «none», «neither», «never», «nobody» have been replaced by the attribute «negtag_» in front of the following word. Thus, «none of» becomes «negtag_of». Finally, the text is divided into bags of words of one or two words, thus considering the impact that two consecutive words can have on the meaning of a sentence.For textual analysis, we chose to use the dictionary method. A dictionary is a lexicon containing words that are associated with a positive or negative score. For this method, we simply look for each word in the text if the word is present in the dictionary used and assign its score if necessary. This method seems very simple at first sight but it can be difficult to implement. Indeed, depending on the context of the study, words can have different meanings. Thus, in our context, a dictionary that is not specific to finance (Harvard IV) is not appropriate and neither is a dictionary that is not specific to the language of social networks. Indeed, it is very complicated to analyse social network data with dictionaries designed for other uses such as the analysis of press articles or corporate annual reports (Loughran and Macdonald), even if this one is designed for finance, as the sentence constructions are very different.Therefore, Thomas Renault (2017) decided to create his own dictionary using data from the social network Stocktwits, which has the advantage of allowing users to label their own posts with bull, bear, or neutral. What makes it different from the other two mentioned above is that it was designed with texts from social networks. For example, it considers emojis but also some expressions like «lol» very used on this type of platform. For the rating of a tweet, we simply take the sum of the scores of the words in it. The date of each tweet is calibrated so that tweets occurring after the closing time of the New York Stock Exchange are associated with the next day. In addition, tweets are weighted by the number of retweets and quotes as shown in <i>formula 1</i>.