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diff --git a/‎third_party/Pipfile
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@@ -176,3 +176,19 @@ \subsection{Simple Debugger}
 
 The output is too long to be covered here.
 Feel free to run the snippet from your terminal and get a sense of how amazing and helpful this little package can be.
+
+
+\subsection{Text Analysis}
+
+If you want a simple way to perform text analysis, you can use \lstinline{TextBlob}.
+The following snippet shows you a sample usage.
+Make sure to read the docs of the package as it provides functionality for translations and further analysis, too.
+
+\lstinputlisting[caption=text\_analysis.py]{../third_party/text_analysis.py}
+
+\textbf{Note:} Make sure to download the needed data before hand, otherwise an exception is thrown telling you to download it.
+
+\begin{lstlisting}[caption=Download data using NLTK]
+>>> import nltk
+>>> nltk.download('averaged_perceptron_tagger')
+\end{lstlisting}
@@ -40,6 +40,7 @@ requests = "*"
 xlrd = "*"
 pysnooper = "*"
 cooked-input = "*"
+textblob = "*"
 
 [dev-packages]
 
 
@@ -43,5 +43,6 @@ A collection of useful snippets using third party packages.
 | test_asyncio | Example on how to use asyncio |
 | test_gevent | Demonstrates the usage of gevent |
 | test_tornado | Reveales the usage of tornado |
+| text_analysis | Analyse text using TextBlob |
 | timing_tool | Illustrates the usage of boxx.timeit() reveilling the time a certain code block takes to run |
 | world_bank_data | Using official world bank data to demonstrate the usage of `zipfile` and `csv` |
@@ -0,0 +1,8 @@
+The titular threat of The Blob has always struck me as the ultimate movie
+monster: an insatiably hungry, amoeba-like mass able to penetrate
+virtually any safeguard, capable of--as a doomed doctor chillingly
+describes it--"assimilating flesh on contact.
+Snide comparisons to gelatin be damned, it's a concept with the most
+devastating of potential consequences, not unlike the grey goo scenario
+proposed by technological theorists fearful of
+artificial intelligence run rampant.
@@ -0,0 +1,14 @@
+from pathlib import Path
+
+from textblob import TextBlob
+
+
+path = Path("src/text.txt")
+
+with open(path) as f:
+    text = f.read()
+
+blob = TextBlob(text)
+
+for sentence in blob.sentences:
+    print(sentence.sentiment.polarity)