Create a tokenized document.

str = ["The book was VERY good!!!!""The book was terrible."]; documents = tokenizedDocument(str);

Evaluate the sentiment of the tokenized documents. A score of 1 indicates positive sentiment, a score of -1 indicates negative sentiment, and a score of 0 indicates neutral sentiment.

compoundScores = ratioSentimentScores(documents)

compoundScores =2×11 -1

Sentiment analysis algorithms rely on annotated lists of words called sentiment lexicons. For example, theratioSentimentScoresfunction uses a sentiment lexicon with words annotated with a sentiment score ranging from -1 to 1, where scores close to 1 indicate strong positive sentiment, scores close to -1 indicate strong negative sentiment, and scores close to zero indicate neutral sentiment.

If the sentiment lexicon used by theratioSentimentScoresfunction does not suit the data you are analyzing, for example, if you have a domain-specific data set like medical or engineering data, then you can use your own custom sentiment lexicon. For an example showing how to generate a domain specific sentiment lexicon, seeGenerate Domain Specific Sentiment Lexicon.

Create a tokenized document array containing the text data to analyze.

textData = ["This company is showing extremely strong growth.""This other company is accused of misleading consumers."]; documents = tokenizedDocument(textData);

Load the example domain specific lexicon for finance data.

文件名="financeSentimentLexicon.csv";台= readtable(文件名);头(台)

ans=8×2 tableToken SentimentScore _________________ ______________ {'opportunities'} 0.95633 {'innovative' } 0.89635 {'success' } 0.84362 {'focused' } 0.83768 {'strong' } 0.81042 {'capabilities' } 0.79174 {'innovation' } 0.77698 {'improved' } 0.77176

Evaluate the sentiment using theratioSentimentScoresfunction and specify the custom sentiment lexicon using the'SentimentLexicon'option. A score of 1 indicates positive sentiment, a score of -1 indicates negative sentiment, and a score of 0 indicates neutral sentiment.

compoundScores = ratioSentimentScores(documents,'SentimentLexicon',tbl)

compoundScores =2×11 -1