The quality of a sentiment analysis depends on several factors, especially the fit of the lexicon for a given topic. It has to be taken into account that human assessments of the same text can also differ. In the literature on automatic sentiment analysis, values of 60 to 70% agreement compared to a human control assignment are usually reported. Note: Sorting in the "Sentiment" column is based on the calculated sentiment value, not on the difference between positive and negative words. Tweets that don’t contain any word with a sentiment score are classified as "no sentiment". If the mean value is equal or close to zero, the tweet text is classified as "neutral". If the mean value is negative, the tweet is evaluated as "negative" or "slightly negative", if the mean value is positive, the tweet is evaluated as "positive" or "slightly positive". The mean value is calculated from the sentiment scores of the evaluated words in a tweet. In case of modal verbs, such as "can", "should", etc., the sentiment scores of the following words are reduced.In case of negation, the scores of the following 3 words are reversed, for example, the statement "I was not very happy" is classified as negative.In addition, two rules are applied to optimize the evaluation of sentiment: If a lemma is found for the word and this lemma is contained in the sentiment lexicon, the sentiment score of the lemma is used for the word. If the word is not in the lexicon, MAXQDA looks up the word in a lemma list. When analyzing a tweet, MAXQDA checks each word whether it is contained in the lexicon and assigns the sentiment score to this word (hashtags and stop list words are ignored if desired). This value is negative for words with negative connotations, close to zero for neutral words, and positive for words with positive connotations. MAXQDA uses a lexicon to evaluate sentiments, which contains a sentiment score for each word in the lexicon.
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