![]() In the literature on automatic sentiment analysis, values of 60 to 70% agreement compared to a human control assignment are usually reported. Please 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 word 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. If the number is negative, the negative words predominate. Difference: Difference between positive and negative words.(Negative) Words: Number of words that were evaluated as negative.(Positive) Words: Number of words that were evaluated as positive.tweets that do not contain words with a sentiment value are marked with "No sentiment". Sentiment: Contains the rating of the tweet in five levels from "negative" to "positive".After clicking OK, the sentiment analysis is performed and four columns are added to the tweet table:.Also hashtags that correspond to a stop word are ignored. Ignore hashtags – if this option is enabled, hashtags, such as #bestever, are not taken into account when evaluating sentiment.Īpply stop word list – if this option is enabled, all words from the selected stop word list will be ignored when evaluating the sentiment. After clicking Analyze Tweets, the following analysis view appears:Īt the top, set the language of the tweets so that MAXQDA will use the appropriate resource for sentiment evaluation (currently: English and German, more languages will follow).In the dialog that appears, select all the Twitter documents you want to include in the analysis.Open the analysis view for tweets via Analysis > Analyze Tweets.Perform sentiment analysisĪ prerequisite for performing the sentiment analysis is that you have already imported tweets into the currently opened MAXQDA project. The tweets can be sorted and filtered according to their sentiments, and the tweets can also be automatically coded with their sentiments for further analysis. This automatically evaluates whether tweet content is to be assessed as negative, neutral, or positive. With MAXQDA you can perform a sentiment analysis of tweets. Please note that the content within this manual refers solely to data acquired during the period when the service was still named "Twitter." As a result, we have chosen to maintain the original terminology throughout. In July 2023, the platform previously known as "Twitter" underwent a renaming to "X". Nonetheless, any Tweets already imported into a MAXQDA project can still be analyzed using the “Analyse Tweets” workspace. IMPORTANT: Following changes in the Twitter API regulations made by Twitter in April 2023, MAXQDA no longer supports the „Twitter import“ function.
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