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The following tutorial will introduce two +methods, which will show you in a quick way what are the most frequent word in +our dataset. + +## 1. Top words + +The easiest way to find out which words are most important is to have a look at +their absolute occurence in the set. This is simply how often a word/token is in +your set. Before texthero that easy task was quite complex to code. You first +needed to write your own tokenizer, generate a DocumentTerm matrix with the +CountVectorizer for example, then sum over one axis and sort in the end. This +process is now simplyfied by Texthero. +```python +>>> # save the dataset in df +>>> import texthero as hero +import pandas as pd +>>> df = pd.read_csv("https://github.com/jbesomi/texthero/raw/master/dataset/bbcsport.csv") +>>> # now we will extract all top words +>>> top_words = hero.top_words(df["text"]) +>>> top_words.head() +the 12790 +to 7051 +a 5516 +in 5271 +and 5259 +Name: text, dtype: int64 +``` +However we can now see, that from the most common words we don't get so much information, as we have hoped for. This is, that the english language does not only contain relevant words with information, but also stopwords, which purpose is, to connect important words to gramaticaly complete sentences. To extract the most relevant parts of the texts, we will now first clean it with the texthero clean function and then look again at the topwords. + +```python +>>> df["clean"] = hero.clean(df["text"]) +>>> top_words = hero.top_words(df["clean"]) +said 1338 +first 790 +england 749 +game 681 +one 671 +Name: clean, dtype: int64 +``` + +Now we can see, that for example most of the texts contain "england" and are about "games". That is now quite useful for further analysis and can be done in just two lines of code. + +## 2. Wordcloud + +But the data frame is still quite technical and less graphical. This can be improved by generating a WordCloud. Texthero has a build-in function, which calls the word_cloud package API to generate the picture. A wordcloud consits of the top words in our dataset arranged in a cloud, where the more frequent words as visualised bigger than the less frequent ones. When executing the following lines in a jupyter notebook, it will show you a wordcloud with the most common words + +```python +>>> import texthero as hero +>>> import pandas as pd +>>> df = pd.read_csv("https://github.com/jbesomi/texthero/raw/master/dataset/bbcsport.csv") +>>> df["clean"] = hero.clean(df["text"]) +>>> hero.wordcloud(df["clean"]) +``` +![](/img/wordcloud.png) + +Here we can easily recognise the popular words from before, as they are printed bigger than the others. From f2c09e86fd49fdfca7c2f92e31c5a3b52b8ec3c7 Mon Sep 17 00:00:00 2001 From: Maximilian Krahn Date: Sat, 5 Sep 2020 18:11:59 +0200 Subject: [PATCH 2/5] updated tutorial --- website/docs/getting-started-visulization.md | 43 +++++++++----------- 1 file changed, 19 insertions(+), 24 deletions(-) diff --git a/website/docs/getting-started-visulization.md b/website/docs/getting-started-visulization.md index 44ba6499..54f4ec8d 100644 --- a/website/docs/getting-started-visulization.md +++ b/website/docs/getting-started-visulization.md @@ -1,44 +1,39 @@ # Visualisation -Now, before you start processing your dataset, you might want to visualize your -data first in order to get the gist of the data and to choose, which NLP toolkit -and models will be most suitable. The following tutorial will introduce two -methods, which will show you in a quick way what are the most frequent word in -our dataset. +Now, before you start processing your dataset, you might want to visualize your data first in order to get the gist of the data and to choose, which NLP toolkit and models will be most suitable. The following tutorial will introduce two methods. Those will show you in a quick way the most frequent words in our dataset. ## 1. Top words -The easiest way to find out which words are most important is to have a look at -their absolute occurence in the set. This is simply how often a word/token is in -your set. Before texthero that easy task was quite complex to code. You first -needed to write your own tokenizer, generate a DocumentTerm matrix with the -CountVectorizer for example, then sum over one axis and sort in the end. This -process is now simplyfied by Texthero. + +The easiest way to find out the most important, is, to have a look at +their absolute occurence in the set. This is simply how often a word/token occurs in your set. Before Texthero that easy task was quite complex to program. You first needed to write your own tokenizer, generate a DocumentTerm matrix with the CountVectorizer for example, then sum over one axis and sort in the end. This process is now simplyfied by Texthero. + ```python >>> # save the dataset in df >>> import texthero as hero -import pandas as pd +>>> import pandas as pd >>> df = pd.read_csv("https://github.com/jbesomi/texthero/raw/master/dataset/bbcsport.csv") >>> # now we will extract all top words >>> top_words = hero.top_words(df["text"]) >>> top_words.head() -the 12790 -to 7051 -a 5516 -in 5271 -and 5259 +the 12790 +to 7051 +a 5516 +in 5271 +and 5259 Name: text, dtype: int64 ``` -However we can now see, that from the most common words we don't get so much information, as we have hoped for. This is, that the english language does not only contain relevant words with information, but also stopwords, which purpose is, to connect important words to gramaticaly complete sentences. To extract the most relevant parts of the texts, we will now first clean it with the texthero clean function and then look again at the topwords. + +However, we can now see, that from the most common words we don't get so much information, as we have hoped for. This is, that the english language does not only contain relevant words with information, but also stopwords, which purpose is, to connect important words to gramaticaly complete sentences. To extract the most relevant parts of the texts, we will now first clean it with the texthero `clean` function and then look again at the topwords. ```python >>> df["clean"] = hero.clean(df["text"]) >>> top_words = hero.top_words(df["clean"]) -said 1338 -first 790 -england 749 -game 681 -one 671 +said 1338 +first 790 +england 749 +game 681 +one 671 Name: clean, dtype: int64 ``` @@ -47,7 +42,6 @@ Now we can see, that for example most of the texts contain "england" and are abo ## 2. Wordcloud But the data frame is still quite technical and less graphical. This can be improved by generating a WordCloud. Texthero has a build-in function, which calls the word_cloud package API to generate the picture. A wordcloud consits of the top words in our dataset arranged in a cloud, where the more frequent words as visualised bigger than the less frequent ones. When executing the following lines in a jupyter notebook, it will show you a wordcloud with the most common words - ```python >>> import texthero as hero >>> import pandas as pd @@ -55,6 +49,7 @@ But the data frame is still quite technical and less graphical. This can be impr >>> df["clean"] = hero.clean(df["text"]) >>> hero.wordcloud(df["clean"]) ``` + ![](/img/wordcloud.png) Here we can easily recognise the popular words from before, as they are printed bigger than the others. From e8654341acaa7189cf220ddc437ea6de3a781b09 Mon Sep 17 00:00:00 2001 From: Maximilian Krahn Date: Sat, 5 Sep 2020 18:12:06 +0200 Subject: [PATCH 3/5] fix black issues --- .travis.yml | 2 +- setup.cfg | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index f913f183..c76284b3 100644 --- a/.travis.yml +++ b/.travis.yml @@ -20,7 +20,7 @@ jobs: env: PATH=/c/Python38:/c/Python38/Scripts:$PATH install: - pip3 install --upgrade pip # all three OSes agree about 'pip3' - - pip3 install black + - pip3 install black==19.10b0 - pip3 install ".[dev]" . # 'python' points to Python 2.7 on macOS but points to Python 3.8 on Linux and Windows # 'python3' is a 'command not found' error on Windows but 'py' works on Windows only diff --git a/setup.cfg b/setup.cfg index d6103b02..3f86e7f3 100644 --- a/setup.cfg +++ b/setup.cfg @@ -41,7 +41,7 @@ install_requires = # TODO pick the correct version. [options.extras_require] dev = - black>=19.10b0 + black==19.10b0 pytest>=4.0.0 Sphinx>=3.0.3 sphinx-markdown-builder>=0.5.4 From 44e3d9efe26e8c06b4e336c6df10fbd6b323a668 Mon Sep 17 00:00:00 2001 From: Maximilian Krahn Date: Tue, 22 Sep 2020 22:12:08 +0200 Subject: [PATCH 4/5] added items to sidebar --- website/docs/getting-started-visulization.md | 5 +++++ website/sidebars.json | 3 ++- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/website/docs/getting-started-visulization.md b/website/docs/getting-started-visulization.md index 54f4ec8d..0b465cf8 100644 --- a/website/docs/getting-started-visulization.md +++ b/website/docs/getting-started-visulization.md @@ -1,3 +1,8 @@ +--- +id: getting-started-visualization +title: Getting started - visualization +--- + # Visualisation Now, before you start processing your dataset, you might want to visualize your data first in order to get the gist of the data and to choose, which NLP toolkit and models will be most suitable. The following tutorial will introduce two methods. Those will show you in a quick way the most frequent words in our dataset. diff --git a/website/sidebars.json b/website/sidebars.json index bca3356b..e8f9a647 100644 --- a/website/sidebars.json +++ b/website/sidebars.json @@ -1,7 +1,8 @@ { "docs": { "Getting Started": [ - "getting-started" + "getting-started", + "getting-started-visualization" ] }, "api": { From d2f858e03927f72a3a5579590b51bebb250cb962 Mon Sep 17 00:00:00 2001 From: Maximilian Krahn Date: Tue, 22 Sep 2020 22:23:41 +0200 Subject: [PATCH 5/5] fixed typo --- website/docs/getting-started-visulization.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/docs/getting-started-visulization.md b/website/docs/getting-started-visulization.md index 0b465cf8..32bf2ddb 100644 --- a/website/docs/getting-started-visulization.md +++ b/website/docs/getting-started-visulization.md @@ -1,9 +1,9 @@ --- id: getting-started-visualization -title: Getting started - visualization +title: Getting started - Visualization --- -# Visualisation +# Visualization Now, before you start processing your dataset, you might want to visualize your data first in order to get the gist of the data and to choose, which NLP toolkit and models will be most suitable. The following tutorial will introduce two methods. Those will show you in a quick way the most frequent words in our dataset.