Introduction
The research analysis is based on the popularity of the complicated types of lyric songs. Through the research, it has been found that these songs are mostly used in a complicated way in most of the countries across the world which brings about a lot of genre complications in the music industry. The research categorically has been based on some popular particularly the hip-pops and rock & pop songs for great analysis. For better analysis of the songs, average levels of the songs were collected in relation to their playing bits and counter character of the individual songs and it was found that they have a complicated form of application. There are certain methods that were used in the same for interpretation of the data based on the songs. The database which was used come up with different 225 types of songs in four different datasets. This gave a certain optimum data point of 2000 and above. The readability score of the songs becomes a major way of measuring whether the song was either complex or just simple composed using simple lyrics. Basically, this research is basically on the determination of how the lyric songs are complex. The analysis of the songs gives the composition of the songs in their lyrical ways in data. The data analysis is basically understood through the way the songs are complex and therefore, the songs are given for the average level of grades. Based on data analysis, 2006 was the highest level of intelligence for songs.
Genre analysis from 2005 to 2014
It is clear that there was much of lyric drop based on the word count in the year 2010. Based on the datasets, raps and pop sounds seem to have taken the position as compared to words in the rock as well as the country. Through this analysis, it is basically very true that pop songs and the rocks become intelligent just more than country songs. It becomes very hard to understand that pops are more intelligent.
Intelligent of the genre covered over 10 years
There is an exception just from the year 2009 and year 2011 which gives an average of 3.3 in relation to country music. This has given light on the fact that rock songs are more intelligent having an approximate of 2.9 and pop has the same 2.9 as well as the R and B for 2.6. This is basically having much of the reasons for the same because there is much of the word which plays length important role than the country which is mostly involved in the same. In this case, there were much of repeated 100 times in the row. Through this, it is very true mimic sounds are basically used in the lyric but this does not matter how much the same as is used in the pop. Syllables in the play songs become very important in the interpretation of the songs. Some songs such as country give a full big type of word count giving admirable sounds. It is very true that there have been very big differences between the grades and length of the songs giving them a negative view of the whole lyric. In the year 2007, it is found to be the year of less top ranks of songs. The research gives the analysis of the 2005 and 2006 raise of the curiously in wondering the position of the songs period time. The trend of the song flow in 10 years of application is basically based on the way they were captured from different years just as mentioned in the datasets. It is very true that the trend of the song categorization is given in different types of identification and in particular country songs.
The future of the songs
The future of the songs is basically based on the perfect ways through which we look at the application of the songs from different years of perspective. Since our main area of concern is lyric songs and country songs, in particular, it is very convincing that the same research objectives give a modern meaning to the songs. The calculation of the songs becomes very sensitive in this case since it gives the test through the way different songs operate. The open-source type of data gives the determination of the complexity of the lyric songs and the way they increase their level of popularity. In this research, there were some factors that were held constant such as the marketing level of the investment which influence the normal concern of the songs. The constraint is basically on the songs which have achieved much in billboards and datasets established on the efforts.
Conclusion
In this research, it is the best form of the model for explanation of the variance in the songs indications. The fact Because models of the give predict of the songs trends, it becomes useful for decision making in the determination of which song is important for popularity complexity. In this research, it has been very strong evidence that private types of music utilize most of the datasets for the purpose of collecting popular songs. In my recommendation, I do seek much of the information concerning the variables and impacts which results from the incorporated models of the music and songs of concern. The result given in the analysis gives the position of the songs which are entered in the charts for the prediction of the most complex songs.
