Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a message authored by a user, determining, using a first classifier, that the message contains at least a first word describing positive or negative sentiment and, based thereon, extracting, using a first feature extractor, one or more features of the message, wherein each feature comprises a respective word or phrase in the message and a respective weight signifying a degree of positive or negative sentiment, and determining, using a second classifier that uses the extracted features as input, a score describing a degree of positive or negative sentiment of the message, wherein the first feature extractor was trained with a set of training messages that each was labeled as having positive or negative sentiment.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: extracting, using a first feature extractor, one or more first features from a message, wherein each first feature comprises a respective word or phrase in the message and an associated weight signifying a degree of positive or negative sentiment; extracting, using a second feature extractor, one or more second features from the message, wherein each second feature comprises a distance between a first word and a second word in the message, wherein the first word comprises at least one of a conditional word and an intensifier word, and wherein the second word comprises at least one of a positive sentiment and a negative sentiment; and determining a score describing a degree of positive or negative sentiment of the message based on output of a trained classifier, wherein the extracted first and second features are provided as input to the classifier. 2. The method of claim 1, wherein the classifier was trained with features extracted by the first and second feature extractors from a set of training messages. 3. The method of claim 1, wherein the first feature comprises an emoticon, an emoji, a word having a particular character in a correct spelling form of the word that is repeated consecutively one or more times, a phrase, an abbreviated or shortened word, or a text string with two or more consecutive symbols. 4. The method of claim 1, wherein extracting, using the first feature extractor, one or more first features from the message comprises using an artificial neural network feature extractor to extract the features. 5. The method of claim 1, wherein the classifier comprises a naive Bayes classifier, a random forest classifier, or a support vector machine classifier. 6. The method of claim 1, further comprising: extracting, using a third feature extractor, one or more third features of the message, wherein each of the extracted third features comprises: (i) two or more consecutive words that describe positive or negative sentiment; (ii) a count of words, symbols, biased words, emojis, or emoticons; or (iii) a word having a particular character in the word's correct spelling form that is repeated consecutively one or more times. 7. A system comprising: one or more computers programmed to perform operations comprising: extracting, using a first feature extractor, one or more first features from a message, wherein each first feature comprises a respective word or phrase in the message and an associated weight signifying a degree of positive or negative sentiment; extracting, using a second feature extractor, one or more second features from the message, wherein each second feature comprises a distance between a first word and a second word in the message, wherein the first word comprises at least one of a conditional word and an intensifier word, and wherein the second word comprises at least one of a positive sentiment and a negative sentiment; and determining a score describing a degree of positive or negative sentiment of the message based on output of a trained classifier, wherein the extracted first and second features are provided as input to the classifier. 8. The system of claim 7, wherein the classifier was trained with features extracted by the first and second feature extractors from a set of training messages. 9. The system of claim 7, wherein the first feature comprises an emoticon, an emoji, a word having a particular character in a correct spelling form of the word that is repeated consecutively one or more times, a phrase, an abbreviated or shorted word, or a text string with two or more consecutive symbols. 10. The system of claim 7, wherein extracting, using the first feature extractor, one or more first features from the message comprises using an artificial neural network feature extractor to extract the features. 11. The system of claim 7, wherein the classifier comprises a naive Bayes classifier, a random forest classifier, or a support vector machines classifier. 12. The system of claim 7, wherein the operations further comprising: extracting, using a third feature extractor, one or more third features of the message, wherein each of the extracted third features comprises: (i) two or more consecutive words that describe positive or negative sentiment; (ii) a count of words, symbols, biased words, emojis, or emoticons; or (iii) a word having a particular character in the word's correct spelling form that is repeated consecutively one or more times. 13. An article comprising: a non-transitory computer storage medium having instructions stored thereon that when executed by one or more computers cause the computers to perform operations comprising: extracting, using a first feature extractor, one or more first features from a message, wherein each first feature comprises a respective word or phrase in the message and an associated weight signifying a degree of positive or negative sentiment; extracting, using a second feature extractor, one or more second features from the message, wherein each second feature comprises a distance between a first word and a second word in the message, wherein the first word comprises at least one of a conditional word and an intensifier word, and wherein the second word comprises at least one of a positive sentiment and a negative sentiment; and determining a score describing a degree of positive or negative sentiment of the message based on output of a trained classifier, wherein the extracted first and second features are provided as input to the classifier. 14. The article of claim 13, wherein the classifier was trained with features extracted by the first and second feature extractors from a set of training messages. 15. The article of claim 13, wherein the first feature comprises an emoticon, an emoji, a word having a particular character in a correct spelling form of the word that is repeated consecutively one or more times, a phrase, an abbreviated or shorted word, or a text string with two or more consecutive symbols. 16. The article of claim 13, wherein extracting, using the first feature extractor, one or more first features from the message comprises using an artificial neural network feature extractor to extract the features. 17. The article of claim 13, wherein the classifier comprises a naive Bayes classifier, a random forest classifier, or a support vector machines classifier. 18. The article of claim 13, wherein the operations further comprise: extracting, using a third feature extractor, one or more third features of the message, wherein each of the extracted third features comprises: (i) two or more consecutive words that describe positive or negative sentiment; (ii) a count of words, symbols, biased words, emojis, or emoticons; or (iii) a word having a particular character in the word's correct spelling form that is repeated consecutively one or more times. 19. The method of claim 1, wherein the first word comprises the intensifier word. 20. The system of claim 7, wherein the first word comprises the intensifier word.