题目内容：

Hit songs are big business, so there is an incentive for composers to get those ingredients that might increase their chances of success. But songs are complex mixtures of features. How to analyse them is made more difficult by the fact that what is popular changes over time. But Natalia Komarova, a mathematician at the University of California, Irvine, thinks she has cracked the problem. Her computer analysis suggests that the songs currently preferred by consumers are danceable, party-like numbers. Unfortunately, those actually writing songs prefer something else.

She and her colleagues collected information on music released in Britain between 1985 and 2015. They looked in music “metadata (元数据)’’ that are used by music lovers and are often tapped into by academics. Metadata are information about the nature of a song that can give listeners an idea of what that song is like before they hear it. Dr. Komarova and her team were presented with more than 500,000 songs to detect numerous musical features. The team fed all of this information into a computer and compared the features of songs that had made it into the charts (排行榜) with those of songs that had not.

Overall, the team’s results suggested that chart successes were happier and brighter than the average songs released during the same year. Chart toppers were also more likely than average songs to have been performed by women.

Dr. Komarova used these results to train her computer to try to predict whether a randomly presented song was likely to have been a hit in a given year. The machine correctly predicted success 75% of the time, compared with that from the music database.

Content isn’t everything. As might be expected, circumstances, particularly any fame already attached to a recording artist or artists, had an effect too. But not a huge one. That suggests that musical fame is actually attached to talent, rather than to advertising. And this is a lesson for an industry that some believe is not connected enough to talent.

1.Why is it difficult to tell what makes good music?

A. What people think is popular changes with the time.

B. What some people think popular isn’t for other people.

C. No one cares about what makes popular music.

D. It’s difficult to know the features of popular music.

2.What can we infer from the results of the team’s research?

A. The content of good music can be typed into computers.

B. Good music has the quality to make people think about life.

C. Happier and brighter songs are more likely to make the charts.

D. Analysis can decide in advance whether a song will be popular.

3.What does the underlined word “incentive” in Paragraph 1 mean?

A. Expression.    B. Motivation.

C. Exhibition.    D. Division.

4.What’s the relationship between musical fame and the singer?

A. A famous singer will make any music he/she sings popular.

B. Good music depends on whether the singer is widely advertised.

C. A good female singer can make an average song popular.

D. Talent is more important than fame to make a song popular.