## The volatile Bitcoin

Technical Analysis of last 300 days of Bitcoin price data.

The top rectangangular frame shows the bar chart(green and orange bars) of the bitcoin price. A bar chart represents the OHLC(open, high, low, close) price for a time period (in this case 1 day) for an asset/stock/cryptocoin.

Bollinger Band: You can see a band (the dotted red lines above and below and a grey line in between) around the bitcoin OHLC price. The grey line represents the average price of 20 days. When the bars are above the grey line, the price is being appreciated, when the bars are below the grey line, the price is being depreciated.

Relative Strength Index(RSI): The blue line in second rectangular frame below shows RSI. Relative strength index(RSI) represents when the bitcoin has been overbought (more bought than being sold –> leads to price hike) or when the bitcoin has been oversold (more sold than being bought –> leads to price drop). When the blue RSI line is abobe 80 (check in the left y axis), it’s normally overbought (price rise) and you can see direct correlation of price hike comparing with the price chart above, when the blue RSI line is below 20, it’s normally oversold (price drop). You can see, right now everyone is panicking and everyone is selling, nobody almost is buying, so price is dropping rapidly. Bitcoin has hit the lowest RSI value of the year.

Moving Average Convergence Divergence(MACD): The third rectangular frame below shows MACD value with grey bars, grey dotted line and a red dotted line. The red dotted line is the average price of 26 days and the grey dotted line is the average price of 12 days. When the grey line is above the red line, it means that the short term price is higher than the long term price, but when it is below, it means that current price is being depreciated from the long term accepted price. MACD is another momentum oscillator which is similar to RSI. You can directly see how it is similar by comparing them. It’s probably easier to read the MACD chart than the RSI. Because there’s the zero line (in RSI it probably represents the value 50) when the grey bars are above(upwards) zero line, it means price appreciation, versus when the grey bars are below zero line(downwards), it means price depreciation. As you can see the price is highly depreciated now.

Commodity Channel Index (CCI): The fourth rectangular frame below shows CCI value with red line and filled red polygons. If in between 100 and -100, the price is not taking any huge turn (bull ride or bear drop). The filled polygons above 100 show the extreme price hike, where as the polygons below -100 show the recent extreme price drop. Because of these polygons, it’s very easy to see the price movement with CCI index.

Hope you enjoyed learning some of the common techniques used to understand market price.

If you think it was helpful and you learnt valuable information which can help you guide your investment, you can contribute my effort and encourage me by sending small gift via Bitcoin or Ethereum.

## Fourier Transform

It was a significant discovery in mathematics that any function can be expanded as a sum of harmonic functions (sines and cosines) and the resulting expression is known as Fourier series. A harmonic of repeating signals such as sunusoidal wave is a wave with a frequency that is a positive integer multiple of the frequency of the original wave, known as the fundamental frequency. The original wave is called the first harmonic, the following harmonics are known as higher harmonics. Any function can also be expanded in terms of polynomials and the resulting expression is known as Taylor series. If the underlying forces are harmonic and there possibly exists some periodicity, then the use of harmonic series is more useful than using polynomials as it produces simpler equations. It is possible to discover a few dominating terms from such series expansion which may help identify the known natural forces with the same period.
Let the symbol $h(t)$ represent a continuous function of time. The Fourier transform is a function of
frequency $f$.

$H_T(f) = \int_{-\infty}^{\infty} h(t) e^{2 \pi i f t} dt$
$e^{2 \pi i f t} = cos(2\pi f t) + i sin(2\pi f t)$

The amplitude and the phases of the sine waves can be found from this result. Given data $h(t)$, we can find the Fourier transform $H(f)$ using Inverse Fourier transform.

$h(t) = \int_{-\infty}^{\infty} H_T(f) e^{-2 \pi i f t} dt$

The spectral power $P$ is defined as the square of the Fourier amplitude:

$P_T(f) = |H_T(f)|^2$

However, real data does not span infinite time and most likely be sampled only at a few discrete points over time. Suppose that, we received values of $h(t)$ at times $t_j$, then an estimate of the Fourier transform is made by using summation. The inverse transform is also shown using the summation.

$h_j \equiv h(t_j)$
$H(f) \equiv \sum_{j=0}^{N-1} h_j e^{2 \pi i f t_j}$

The data are desired to be sampled from equally spaced time as nice statistical properties are available in such regular case. If the interval between equally spaced data points is $\Delta t$, then the highest frequency that will appear in the fourier transform is given by the Nyquist-Shannon sampling theorem. The theorem states “If a function $f(t)$ contains no frequencies higher than $f$ Hz, then it is completely determined by giving its ordinates at a series of points spaced $\frac{1}{2f}$ seconds apart”. Therefore, the Nyquist frequency (highest frequency) is given by the following equation.

$f_N = \frac{1}{2\Delta t}$

The lowest frequency is the one that gives one full cycle in the time interval $T$. The other frequencies to evaluate is the multiples ($f_k$) of the low frequency $f_L$. And, also we can derive the symmetric pair of equations. Moreover, if $h(t)$ is band-limited (no frequencies below $f_L$ or above $f_N$), then there is a relationship between the continuous function $h(t)$ and the discrete values $H_k$.

$f_L = \frac{1}{T}$
$f_k = kf_L$
$H_k \equiv \sum_{j=0}^{N-1} h_j e^{2 \pi i jkf_L t_j}$
$h_j = \sum_{k=0}^{N-1} H_k e^{2 \pi i jkf_L t_j}$
$h(t) = \sum_{k=0}^{N-1} H_k e^{2 \pi i kf_L t}$ (when band limited)

## Periodogram

Fourier transform give us the complex numbers and the square of the absolute value of these numbers represent the periodogram. This is the first form of numerical spectral analysis and is used to estimate spectral power. Even though the data points collected are at evenly spaced specific discrete time, it is possible to evaluate periodogram at any frequencies.

## Fast Fourier Transform (FFT)

We can calculate the Fourier transform very efficiently by using FFT. It requires data at equally-spaced time points, and is most efficient when the number of points is an exact power of two. Interpolation is often used to produce the evenly-spaced data which may introduce additional bias and systematic eror. For real data consisting of $N$ data points $y_j$, each taken at time $t_j$, the power spectrum outputs a set of $N+1$ data points. The first and the last data points are the same, and they represent the power at frequency zero. The second through to the $N/2 + 1$ data points represent the power at evenly-spaced frequencies up to the Nyquist frequency. The spectral power for a given frequency is distributed over several frequency bins, therefore an optimum determination of the power requires combining these information and proper investigation of leakage. FFT, generally, calculates the amplitude for a set of frequencies. N/2 complex amplitudes are calculated at N/2 different frequencies. Because, these may not be the true frequencies present in the record, we subtract the mean from the data and then pad it with zeros to overcome this challenge.

## Aliasing

The time series consists of measurements made at a discrete, equally spaced, set of times on some phenomenon that is actually evolving continuously, or at least on a much finer time scale. For example, samples of Greenland Ice represent the temperature every 100 years, but if the sampling is not precisely spaced by a year, we will sometimes measure winter ice, and other times measure summer ice. Even without the existence of long-term variation in the temperature, fluctuations (jumping up and down) in the data can be noticed. So, there can be frequencies higher than the Nyquist frequency associated with the sampling interval. Thus a peak in the true spectrum at a frequncy beyond the Nyquist frequency may be strong enough to be seen(aliased) in the spectrum which may give the impression that a frequency is significant when it is not. Or, a peak may partly obscure another frequency of interest. This phenomenon is known as aliasing.

## Tapering

Fourier transform is defined for a function on a finite interval and the function needs to be periodic. But with the real data set, this requirment is not met as the data end suddenly at t=0 and t=T and can have discontinuities. This discontinuity introduces distortions (known as Gibbs phenomenon) in fourier transform and generates false high frequency in the spectrum. Tapering (using data window) is used to reduce these artificial presence. The data $y=f(t)$ is multiplied by a taper function $g(t)$ which is a simple, slowly varying function, often going towards zero
near the edges. Some of the popular tapers are:

1. Sine taper $g(t) = sin(\pi t/T)$
2. Hanning (offset cosine) taper $g(t) = \frac{1}{2}(1-cos(2\pi t/T))$
3. Hamming taper $g(t) = 0.54 - 0.46cos(2 \pi t/T)$
4. Parzen or Bartlett (triangle) window $g(t) = 1 - (t - T/2)/(T/2)$
5. Welch (parabolic) window $g(t) = 1 - (t - T/2)^2/(T/2)^2$
6. Daniell (untapered or rectangular) window $g(t) = 1$

The frequency resolution in the spectrum of the tapered data is degraded. If the primary interest is the resolution of peaks, then the untapered periodogram is superior. However, tapering significantly reduces the sidelobes and also the bias applied to other nearby peaks by the sidelobes of a strong peak. Because, the taper functions are broad and slowly varying and their fourier transform FT(g) are narrow. The effect of tapering the data is to convolve the fourier transform of the data with the narrow fourier transform of the taper function which amounts to smoothing the spectral values.

$FT(fg) = FT(f) * FT(g)$

<

p style=”text-align:justify;”> // Sine taper t <- seq(0,1, by=0.01) T <- 1 g <- sin(pi * t * T) plot(t, g, t='l', col=1, ylab='g(t)')

// Hanning (offset cosine) taper
g2 <- 1/2 * (1-cos(2*pi*t/T))
lines(t, g2, t=’l’, col=2)

// Hamming
g3 <- 0.54 – 0.46 * cos(2*pi*t/T)
lines(t, g3, t=’l’, col=3)

// Parzen or Bartlett (triangle) window
g4 0.5, 1 – (t-T/2)/(T/2), 2*t)
lines(t, g4, t=’l’, col=4)

// Welch (parabolic) window
g5 <- 1 – (t-T/2)^2/(T/2)^2
lines(t, g5, t=’l’, col=5)

// Daniell window
g6 <- rep(0.5, length(t))
g6 <- ifelse(t <= 0.2, 0, g6)
g6 = 0.8, 0, g6)
lines(t, g6, t=’l’, col=6)

legnd = c(‘Sine’, ‘Hanning’, ‘Hamming’, ‘Bartlett’, ‘Welch’, ‘Daniell(20%)’)
legend(‘topleft’, legend=legnd ,col=1:6, lty=1, cex=0.75)

## Multitaper Analysis

We apply taper or data window to reduce the side lobes of the spectral lines. Basically we want to minimize the leakage of power from the strong peaks to other frequencies. In multitaper method, several different tapers are applied to the data and the resulting powers then averaged. Each data taper is multiplied element-wise by the signal to provide a windowed trial from which one estimates the power at each component frequency. As each taper is pairwise orthogonal to all other tapers, the windowed signals provide statistically independent estimates of the underlying spectrum. The final spectrum is obtained by averaging over all the tapered spectra. D. Thomson chose the Slepian or discrete prolate spheroidal sequences as tapers since these vectors are mutually orthogonal and possess desirable spectral concentration properties. Multitaper method can suppress sidelobes but have higher resolution. If we use few tapers, the resolution won’t be degraded, but then sidelobe reduction won’t happen much. So, there is a trade-off which is often misunderstood.

## Blackman-Tuckey Method

Blackman and Tuckey prescribed some techniques to analyze a continuous spectrum that was biased by the presence of sidelobes of strong peaks in the ordinary periodogram. Blackman-Tuckey(BT) method was developed before 1958, prior to the FFT(Fast Fourier Transform) method. A discrete fourier transform of N points would
require the calculation of $N^2$ sines and cosines. With the slower computer in the pre-FFT days, the calculation of fourier transform was thus expensive. BT method has reduced the time by reducing the size of the dataset by a factor of the lag in the autocorrelation calculation. BT method is based on a fundamental theorem of Fourier transform that the Fourier transform of a correlation is equal to the product of the Fourier transforms. The correlation of two functions $g(t)$ and $h(t)$ is given by the first equation below.

$C(\tau)=g\otimes h= \int_{-\infty}^{\infty} g(t) h(t+\tau) d\tau$
$FT(g\otimes h) = FT(g) FT(h)$

When g = h, it is called Wiener-Khintchine theorem. Here, P is the spectral power.

$FT(g\otimes g) = |FT(g)|^2 = P$

The algorithm in BT method calculates partial autocorrelation function, defined by

$A_{BT}(\tau) = \int_{0}^{N/l} f(t+\tau)f(t) dt$

Here, N is the length of the data set but we integrate only upto $N/l$. $l$ is associated with the lag. When $l=3$ (recommended by Blackman and Tuckey) is used, we say that “a lag of 1/3” is used. Now the fourier transform of partial autocorrelation function $A_{BT}$ gives us the spectral power. Moreover, the symmetric property of the partial autocorrelation function $(A(-\tau) = A(\tau))$ saves half of the computation time.

$FT(A_{BT}) = \int_{-\infty}^{\infty} e^{2\pi i ft } A_{BT}(\tau) d \tau = P_{BT}(f)$
$P_{BT}(f) = 2 \int_{0}^{\infty} cos(2\pi f) A_{BT}(\tau)$

If $l=1$, then it is basically the full autocorrelation function $A(\tau)$ and gives the same answer as the ordinary periodogram.

$P(f) = 2 \int_{0}^{\infty} cos(2\pi f) A(\tau) = FT(A)$

Because we are using partial correlation function instead of the full correlation, the spectral power function gets smoother. Therefore, we lose resolution in the BT method. However, it averages the sidelobes into the main peak, and thereby gives a better estimate of the true power. The smoothing in BT method is different from the smoothing when we use a taper. With a taper, the fourier transform is smoothed, where as with Blackman-Tukey, it is the spectral power which gets smoothed. A spectral amplitude that is rapidly varying will be averaged to zero with a taper. But in BT method, a rapidly varying amplitude does not necessarily average to zero, since the process of squaring can make the function positive over the region of smoothing. The tapering does not
average the sidelobes into the main peak. Because, shift in the time scale behaves like phase modulation. The sidelobes, when tapering is applied, will not have the same phase, and if averaged in amplitude, they can reduce the strength of the peaks. A major challenge in the BT method is that we will have to estimate the proper lag to use before doing all the calculations. Blackman and Tukey recommended starting with the value 1/3 for the lag.

## Lomb-Scargle Periodogram

The classic periodogram requires evenly spaced data, but we frequently encounter with unevenly spaced data in paleoclimatic research. Lomb and Scargle showed that if the cosine and sine coefficients are normalized separately, then the classic periodogram can be used with unevenly spaced data. If we have a data set $(t_k, y_k)$, we first calculate the mean and variance:

$\bar{y} = \frac{1}{N} \sum_{k=1}^{N}y_k$
$\sigma^2 = \frac{1}{N-1} \sum_{k=1}^{N}[y_k - \bar{y}]^2$

For every frequency f, a time constant $\tau$ is defined by

$\tau = \frac{ \sum_{k=1}^{N}sin(4\pi f t_k)}{\sum_{k=1}^{N}cos(4\pi f t_k)}$

Then the Lomb-Scargle periodogram of the spectral power $P(f)$ at frequency f is given by

$P(f) = \frac{1}{2\sigma^2}\frac{ \sum_{k=1}^{N}(y_k – \bar{y} ) [cos(2\pi f (t_k-\tau))]^2}{\sum_{k=1}^{N}cos^2(2\pi f (t_k-\tau))} + \frac{ \sum_{k=1}^{N}(y_k – \bar{y} ) [sin(2\pi f (t_k-\tau))]^2}{\sum_{k=1}^{N}cos^2(2\pi f (t_k-\tau))}$

With evenly spaced data, two signals of different frequencies can have identical values which is known as Aliasing. That is why the classic periodogram is usually shown with the frequency range from 0 to 0.5, as the rest is a mirrored version. But with Lomb-Scargle periodogram, the aliasing effect can be significantly reduced.

## Maximum Likelihood Analysis

In maximum likelihood method, we adjust the parameter of the model and ultimately find the parameters with which our model have the maximum probability/likelihood of generating the data. To estimate the spectral power, we first select a false alarm probability and calculate the normalized periodogram. We identify the maximum peak and test it against the false alarm probability. If the maximum peak meets the false alarm test, we determine the amplitude and phase of the sinusoid representing the peak. Then we subtract the sinusoidal curve from the data which also removes the annoying sidelobes associated with that peak. After peak removal, the variance in the total record is also reduced. Now, with the new subtracted data, we continue finding the other stronger peaks following the same procedure. We stop when a peak does not meet the false alarm test. We need to carefully choose the false alarm probability, as if it is too low, we can miss some significant peaks; it is too low, we can mislabel noise as peaks.

## Maximum Entropy Method

It is assumed that the true power spectrum can be approximated by an equation which has a power series. This method finds the spectrum which is closest to white noise (has the maximum randomness or “entropy”) while still having an autocorrelation function that agrees with the measured values – in the range for which there are measured values. It yields narrower spectral lines. This method is suitable for relatively smooth spectra. With noisy input functions, if very high order is chosen, there may occur spurious peaks. This method should be used in conjuction with other conservative methods, like periodograms, to choose the correct model order and to avoid getting false peaks.

## Cross Spectrum and Coherency

If a climate proxy $a(t)$ is influenced or dominated by a driving force $b(t)$, we can use cross spectrum to see if their amplitudes are similar. Cross spectrum is given by the product of the fourier transform.

$C(f) = A(f) B^*(f)$

where A is the Fourier transform of a and B is the complex conjugate of the fourier transform of b. If we want to know whether two signals are in phase with each other, regardless of amplitude, then we can take the cross spectrum, square it, and divide by the spectral powers of individual signals using the following equation for coherency. Coherency measures only the phase relationship and is not sensitive to amplitude which is a big drawback.

$c(f) = \frac{|C(f)|^2}{P_a(f) P_b(f)}$

Coherency is valuable if two signals that are varying in time, stay in phase over a band of frequencies instead of a single frequency. Therefore, a band of adjacent frequancies are used in the averaging process to compute coherency:

$coherency(f) = \gamma^2(f) = \frac{||^2}{ }$

## Bispectra

In bispectra, coherency relationship between several frequencies are used. A bispectrum shows a peak whenever (1) three frequencies $f_1$, $f_2$ and $f_3$ are present in the data such that $f_1 + f_2 = f_3$ and (2) the phase relationship between the three frequencies is coherent for at least a short averaging time for a band near these frequencies. If the nonlinear processes in driving force (e.g. eccentricity or inclination of the orbit of earth) has coherent frequency triplets, then the response (i.e. climate) is likely to contain same frequency triplet. For example, $\delta ^{18}O$ is driven by eccentricity, we should be able to find eccentricity triplet. Thus, by comparing the bispectrum plot of climate proxy with the bispectrum plot of the driving forces, we can verify the influences of driving forces.

## Monte Carlo Simulation of Background
Monte carlo simulation is extremely useful to answer the questions like whether the data is properly tuned or not, whether the timescale is incorrect, whether some spectral power is being leaked to adjacent frequencies, whether the peak has real structure and also to understand the structures near the base of the peak (a shoulder) in a spectral analysis. Generally monte carlo simulation is run multiple times. For each simulation, a real signal(sinusoidal wave) is generated, then random background signal is added, then the spectral power is calculated to look for shoulders. In this way, the frequency of the shoulder occurence can be measured and the randomness can be realized. It is important to create background that behaves similarly to the background in real data. Dissimilar background will cause false conclusion. We also need to estimate the statistical significance of the peaks very carefully.

(This article is a quick review of the fourier spectral analysis from the book “Ice Ages And Astronomical Causes- (Data, Spectral Analysis and Mechanics) by Richard A. Muller and Gordon J. MacDonald

## Statistics and Data Exploration: Quantiles, probability distribution, Box plot and Q-Q (Quantile-Quantile) plot

Statistics and Data Exploration: Quantiles, probability distribution, Box plot and Q-Q (Quantile-Quantile) plot

### Quantiles

What are quantiles in statistics?

If the data is sorted from small to big, Quantiles are the points which divide the data/samples into equal sized, adjacent subgroups. Every data sample has maximum value, minimum value, median value(the middle value after you sort the data). The middle value in the sorted data is the 50% quantile because half of the data are below that point and half above that point. A 25% quantile is the cut point in the data where 1/4 -th of the data is below the point. IQR is inter-quartile range which contains half of the data which contains the median and are higher than the 25% low-value data point but less than the 25% high-value data point.

### Box Plot

A box-plot can be a good representation to show the quantiles. Box plot can take different shapes depending on the data. Here is an example:

### Example of Discrete/Continous Probability Distribution

In the figure below, you can see different frequency distribution. The blue data samples have most of it’s data near (0,1) interval, it’s left skewed. Check how the blue box is shifted to the left. The green data samples are normally distributed, meaning most of the data points are centered around zero. It also looks balanced. We find normal distribution in nature and in biological and social phenomena very often. The orange one shows almost a uniform distribution, where the data is spreaded across the range. And lastly a right skewed data. These are all discrete data points with discrete probability distribution. There are also very well known continuous probability distribution with continuous probability density function(https://en.wikipedia.org/wiki/List_of_probability_distributions#Continuous_distributions).

###### (Image source: https://www.otexts.org/node/620)

Below we can see the quantiles for the normal distribution- the cut points which divide the continuous range of points in equal probability area. The area over an interval (in x axis) under a continuous probability density function (like the normal distribution function below) represents the probability of the data falling into that range. In this case, the IQR is the blue box; data point in that interval has 50% probability of occurrence.

### Q-Q plot

We can use Q-Q plot to graphically compare two probability distributions. Q-Q plot stands for Quantile vs. Quantile plot. In Q-Q plotting, we basically compute the probabilities assuming a certain distribution (e.g. normal, gamma or poisson distribution) from the data and then compare it with theoritical quantiles. The steps used in Q-Q plotting is:

1. Sort the data points from small to large
2. For n data points, find n equally spaced points which serve as the probability using $\frac{k}{n+1}$ where $k=1, 2, ..., n$
3. Look at the data points, possibly plot it and assume the underlying probability distributions. Using the probabilities from the step 2, now you can calculate quantiles. Like in R language, you can use the quantile functions like qnorm or qgamma or qunif from the stats package.
4. Now plot by putting the calculated quantiles in step 3 in x axis and putting the sorted data points in the y-axis. If the data points stay close to the $y=x$ line, that means your assumption of the probability distribution was correct.

Below you can see one example, where the normal distribution is assumed for the ozone data. T

Now you can see the gamma distribution fits better to the ozone data than the normal distribution.

This is how you can check different probability distribution for your data using simple Q-Q plot. There is a fantastic Q-Q plot tutorial from which I collected the above image. For further reading, please check https://www.r-bloggers.com/exploratory-data-analysis-quantile-quantile-plots-for-new-yorks-ozone-pollution-data/ and https://www.r-bloggers.com/exploratory-data-analysis-quantile-quantile-plots-for-new-yorks-ozone-pollution-data/

## Chance

It’s 2018. The first book to try to formulate probability/chance was by Abraham de Moivre in exactly 300 years ago in 1718.

If you roll two dices, can you get two numbers that sum to 14? No. Coz the maximum number in each dice is 6. But can you get a number 11? Yes. That’s probable. How much probable? Can we measure? These are some basic probability that every science student learns in high school. But the underlying philosophy here is striking:
“Not everything is equally probable. Some are more likely than others. Some arguments, ideas are better than others, but based on some established ideas or agreed upon assumptions. How do we measure?”

What are your chances of waking up tomorrow? Is it 100%? What’s the chance that your relationship will last? Are these even measurable? What does it mean when you say I hope? Does that automatically translate into a highly probable future? Why don’t you get that million dollar lottery? Can you win some bucks at those Vegas gambling playing poker or blackjack? 😉 What’s your chance that you will win the game? It wasn’t that easy for mathematicians and statisticians to formulate what it really means by what’s probable,what’s expected in the world of uncertainty we live in and deal with. And in science, who doesn’t deal with some basic probability equations in their data.
.

I absolutely admire the talk by Dr. Ana at Lawrence Livermore National Lab on “Understanding the world through statistics.” who introduced me to this book.

“The best thing about being a statistician is that you get to play around everyone’s backyard.” – The great statistician John Tukey.

May be I can make a new phrase for CS programmer too..haha.

“The best thing about being a computer programmer is that you get to make toys for everyone.” LoL.

Basketball statistics and why Stephen Curry attempts more 3 point shots than 2 point shots:

## Looking Inside Eminem’s Lyrics – part 1

I started analyzing the lyrics of Eminem. My initial interest is that what are the most common words Eminem has been using in his lyrics. I collected the name of all his 287 songs from this link. Then I collected the lyrics using python sontext library which collects lyrics from lyricwiki.org. I have become successful in gathering lyrics for 223 songs of Eminem out of those 287 song names using my python code. After gathering the lyrics, I had to process the text in the lyrics. Normally in language processing tasks, we do chunking, lemmatization, stemming, spelling error check. I have used NLTK library for all these. Actually I had to avoid doing lemmatization as it was chopping off lots of interesting words in its existing form. And also I created a banned words list as Eminem has used a lot of ‘na’, ‘wa’, ‘oh’ kind of words which semantically doesn’t have much meaning.  Then I used NLTK word frequency method to find out the frequency of words. The top 20 words used were

[(u’like’, 1375), (u’get’, 1049), (u’got’, 907), (u’shit’, 740), (u”’cause”, 729),

(u’know’, 701), (u’back’, 674), (u’fuck’, 671), (u’eminem’, 593), (u’go’, 557),

(u’see’, 514), (u’one’, 497), (u’say’, 476), (u’never’, 430), (u’bitch’, 428),

(u’man’, 428), (u’let’, 422), (u’time’, 411), (u’come’, 392), (u’think’, 361)]

And yeah apparently Eminem has cursed a lot in his songs. As you can see in the plot below for the word “shit” (rank:4), “fuck” (rank:8), “bitch” (rank:15).

Then the word ‘love’ has been used 282 times just bit less than the word ‘ass’ which was used 295 times. You can see the word ‘dre’ has been used a lot and it’s most likely Dr. Dre who worked with Eminem. The word ‘man’ is used more than the word ‘girl’. The word ‘hate’ is used less than the word ‘love’, only 116 times. Here’s two more plots for word frequency.

Anyway, simple bag of words probably don’t give good representation of a particular song. For example, the word love can be used in a sentence “I love you” but then also “I don’t love you” which has completely opposite meaning. But here they are counted all together. Before contextual analysis, I was just thinking about doing another frequency analysis according to Russel’s model of mood. You basically divide the xy-plane into four orthogonal regions as you can see in the image below.

I want to see where eminem’s music in general fall in this emotional plane. There’s more interesting analysis I can do later on using word vector and other new NLP techniques. I’ll eventually look into other artists, other genres and try to find whether there are different patterns in how the words are chosen and the kind of emotion certain songs may generate.

Code for getting Lyrics:

import lxml
from lxml import html
import requests

import pickle

import numpy as np
import libsongtext
from libsongtext import lyricwiki
from libsongtext import utils

import pprint as pp

artist_name = 'eminem'

url = 'http://www.spin.com/2014/10/eminem-every-song-ranked/'
#f = urllib.urlopen(url)
f = requests.get(url)

tree = html.fromstring(html_page)

song_name_xpath=tree.xpath('//div[@class="article-content clearfix"]//strong/a')
song_names=[]

num = 1
lyrics_list = {}
lyrics_not_found_list = []
success_lyrics_cnt = 0
for s in song_name_xpath:
song = ''.join(s.text.encode('ascii', 'ignore').strip())
song_names.append(song)

print 'No. ' + str(num)
num = num + 1
print 'track : ' + song

try:
args = {}
args['artist'] = artist_name
args['title'] = song.strip()
title = args['title']
if not lyrics_list.get(title):
t = lyricwiki.LyricWikiSong(args)
lyrics = t.get_lyrics()
print "Got Lyrics."
lyrics_list[title] = lyrics
success_lyrics_cnt += 1
except:
lyrics_not_found_list.append(song)
print "Failed to get Lyrics."

print 'Successfully got ', success_lyrics_cnt, ' lyrics out of ', len(song_name_xpath), ' tracks'

def save_obj(obj, path, name):
with open(path + name + '.pkl', 'wb') as f:
pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)

with open(path + name + '.pkl', 'rb') as f:

save_obj(lyrics_list, '/Users/andy/Documents/projects/music/lyrics_database/eminem/', 'eminem_song_lyrics')



Code for word frequency analysis in Lyrics:

import pickle
import string

import nltk
from nltk.tokenize import sent_tokenize
from nltk import word_tokenize
from nltk import sent_tokenize
from nltk.corpus import stopwords

import enchant
from enchant.checker import SpellChecker

eng_dict = enchant.Dict("en_US")

#import lyrics of eminem
eminem_lyrics_pickle_file='/Users/andy/Documents/projects/music/lyrics_database/eminem/eminem_song_lyrics.pkl'
f = open(eminem_lyrics_pickle_file, 'rb')

song_names=lyrics_list.keys()
lyrics= lyrics_list.values()

# english words
#words = set(nltk.corpus.words.words())

#stemmer
porter = nltk.PorterStemmer()
wnl = nltk.WordNetLemmatizer()

def plot_freqdist_freq(fd,
max_num=None,
cumulative=False,
title='Frequency plot',
linewidth=2):
"""
As of NLTK version 3.2.1, FreqDist.plot() plots the counts
and has no kwarg for normalising to frequency.
Work this around here.

INPUT:
- the FreqDist object
- max_num: if specified, only plot up to this number of items
(they are already sorted descending by the FreqDist)
- cumulative: bool (defaults to False)
- title: the title to give the plot
- linewidth: the width of line to use (defaults to 2)
OUTPUT: plot the freq and return None.
"""

tmp = fd.copy()
norm = fd.N()
for key in tmp.keys():
tmp[key] = float(fd[key]) / norm

if max_num:
tmp.plot(max_num, cumulative=cumulative,
title=title, linewidth=linewidth)
else:
tmp.plot(cumulative=cumulative,
title=title,
linewidth=linewidth)

return

stem_tokens = ['ed', 'ies', '\'s' , 'n\'t', '\'m', '--', '\'\'']
banned_words = ['ha', 'wa', 'ta', 'u', 'i', 'ai', 'na', 'ca', '...', '..', '\'em', '\'en', 'wan', '', '',
'oh', 're', '\'re', '\'ne', 'yea', 'yeah', 'ya', 'yah', '\'ve', '\'d', 'wo', 'oh', 'ooh',
'\'ll', 'yo', 'is\\u2026', 'ah', 'wit', 'would', '\\u2019']

#['i\'ma', 'y\'ll']

def synonyms(word):
syns = []
for word in wn.synsets(word):
sim_words = word.similar_tos()
sim_words += word.lemma_names()
for sim in sim_words:
s = sim
if hasattr(s, '_name') :
s = sim._name.split(".")[0]
syns.append(s)

syns = set(syns)
return syns

def stem(word):
for suffix in stem_tokens:
if word in banned_words:
return False

if word == 'suffix' or word.endswith(suffix):
return word[:-len(suffix)]
return word

lyrics_edited = []
chkr = SpellChecker("en_US")

edited_tokens = []
i = 1
for s, l in lyrics_list.items():
print i, ". Processing song: \"", s, "\""
i += 1
# find wrongly spelled words
chkr.set_text(l)
err_words=[]
for err in chkr:
err_words.append(err.word)

#tokenize
tokens = word_tokenize(l)
l_txt = nltk.Text(tokens)

for t in tokens:
tn = t.lower()
#tn = porter.stem(t)
#tn = wnl.lemmatize(tn)

tn = stem(tn)
if tn and tn not in err_words and tn not in stopwords.words('english') and tn not in list(string.punctuation):
edited_tokens.append(tn)

uniq_tokens = set(edited_tokens)

fdist = nltk.FreqDist(edited_tokens)

#Rusell's Model of mood
mood_happy_words = ['Exhilarated', 'Excited', 'Happy', 'Pleasure']
mood_h = []
for ws in mood_happy_words:
for w in synonyms(ws):
mood_h.append(w)

mood_h = list(set(mood_h))

mood_angry_words = ['Anxious', 'Angry', 'Terrified', 'Disgusted']
mood_a = []
for ws in mood_angry_words:
for w in synonyms(ws):
mood_a.append(w)

mood_a = list(set(mood_a))

mood_s = []
for w in synonyms(ws):
mood_s.append(w)

mood_s = list(set(mood_a))

mood_relaxed_words = ['Relaxed', 'Serene', 'Tranquil', 'Calm']
mood_r = []
for ws in mood_relaxed_words:
for w in synonyms(ws):
mood_r.append(w)

mood_r = list(set(mood_r))

`