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    <title>DEV Community: SUBHRANIL DAS</title>
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      <title>How I Extracted Amazon Review Text Data from Kaggle's .bz2 Dataset for Sentiment Analysis</title>
      <dc:creator>SUBHRANIL DAS</dc:creator>
      <pubDate>Fri, 26 Jun 2026 11:49:05 +0000</pubDate>
      <link>https://dev.to/subhranil_das_0d2e0d989a4/how-i-extracted-amazon-review-text-data-from-kaggles-bz2-dataset-for-sentiment-analysis-m5b</link>
      <guid>https://dev.to/subhranil_das_0d2e0d989a4/how-i-extracted-amazon-review-text-data-from-kaggles-bz2-dataset-for-sentiment-analysis-m5b</guid>
      <description>&lt;p&gt;I recently started working on my first NLP and Sentiment Analysis project using the Amazon Reviews dataset on Kaggle.&lt;/p&gt;

&lt;p&gt;I expected to find a normal CSV file that I could load using &lt;code&gt;pandas.read_csv()&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Instead, I found files like this:&lt;/p&gt;

&lt;blockquote&gt;
&lt;p&gt;train.ft.txt.bz2 &lt;br&gt;
test.ft.txt.bz2&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;At first, I was confused.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;What is a &lt;code&gt;.bz2&lt;/code&gt; file?&lt;/li&gt;
&lt;li&gt;How do I read it?&lt;/li&gt;
&lt;li&gt;Why isn't there a CSV file?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;After some searching and experimentation, I discovered that the dataset is stored in a compressed &lt;strong&gt;&lt;code&gt;BZip2&lt;/code&gt;&lt;/strong&gt; format. Fortunately, Python provides a built-in &lt;code&gt;bz2&lt;/code&gt; library that can read these files directly.&lt;/p&gt;

&lt;h2&gt;
  
  
  Step 1: Reading the compressed file
&lt;/h2&gt;

&lt;p&gt;The first step was simply reading the compressed file.&lt;br&gt;
&lt;/p&gt;

&lt;div class="highlight js-code-highlight"&gt;
&lt;pre class="highlight python"&gt;&lt;code&gt;&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;bz2&lt;/span&gt; 
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;pandas&lt;/span&gt; &lt;span class="k"&gt;as&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt; 
&lt;span class="n"&gt;file_path&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;/kaggle/input/datasets/bittlingmayer/amazonreviews/train.ft.txt.bz2&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;
&lt;span class="k"&gt;with&lt;/span&gt; &lt;span class="n"&gt;bz2&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="nf"&gt;open&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;file_path&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;rt&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;encoding&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;utf-8&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="k"&gt;as&lt;/span&gt; &lt;span class="n"&gt;f&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt;
 &lt;span class="n"&gt;lines&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;f&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="nf"&gt;readlines&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; 
&lt;span class="nf"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;lines&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;/div&gt;



&lt;p&gt;The output looked something like this:&lt;br&gt;
&lt;code&gt;__label__2 Stunning even for the non-gamer: This soundtrack was beautiful...&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;At this point, I noticed that every line starts with a label followed by the review text.&lt;/p&gt;
&lt;h2&gt;
  
  
  Step 2: Separating Labels and Reviews
&lt;/h2&gt;

&lt;p&gt;Next, I separated the sentiment label from the review text.&lt;br&gt;
&lt;/p&gt;

&lt;div class="highlight js-code-highlight"&gt;
&lt;pre class="highlight python"&gt;&lt;code&gt;&lt;span class="n"&gt;labels&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;[]&lt;/span&gt;
&lt;span class="n"&gt;sentences&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;[]&lt;/span&gt;

&lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;line&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="n"&gt;lines&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt;
    &lt;span class="n"&gt;label&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="mi"&gt;1&lt;/span&gt; &lt;span class="k"&gt;if&lt;/span&gt; &lt;span class="n"&gt;line&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="nf"&gt;split&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt; &lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;)[&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;__label__2&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt; &lt;span class="k"&gt;else&lt;/span&gt; &lt;span class="mi"&gt;0&lt;/span&gt;
    &lt;span class="n"&gt;sentence&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;line&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="nf"&gt;split&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt; &lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)[&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;

    &lt;span class="n"&gt;labels&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="nf"&gt;append&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
    &lt;span class="n"&gt;sentences&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="nf"&gt;append&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;sentence&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;/div&gt;



&lt;p&gt;The dataset uses:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;
&lt;code&gt;__label__1&lt;/code&gt; → Negative Review&lt;/li&gt;
&lt;li&gt;
&lt;code&gt;__label__2&lt;/code&gt; → Positive Review&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Since machine learning models work better with numerical values, I converted them into:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;
&lt;code&gt;0&lt;/code&gt; → Negative&lt;/li&gt;
&lt;li&gt;
&lt;code&gt;1&lt;/code&gt; → Positive&lt;/li&gt;
&lt;/ul&gt;

&lt;h2&gt;
  
  
  Step 3: Creating a DataFrame
&lt;/h2&gt;

&lt;p&gt;Finally, I converted everything into a Pandas DataFrame.&lt;br&gt;
&lt;/p&gt;

&lt;div class="highlight js-code-highlight"&gt;
&lt;pre class="highlight python"&gt;&lt;code&gt;&lt;span class="n"&gt;df&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="nc"&gt;DataFrame&lt;/span&gt;&lt;span class="p"&gt;({&lt;/span&gt;
    &lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;review&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="n"&gt;sentences&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
    &lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;sentiment&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="n"&gt;labels&lt;/span&gt;&lt;span class="p"&gt;})&lt;/span&gt;

&lt;span class="nf"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;df&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="nf"&gt;head&lt;/span&gt;&lt;span class="p"&gt;())&lt;/span&gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;/div&gt;



&lt;p&gt;Example output:&lt;br&gt;
&lt;/p&gt;

&lt;div class="highlight js-code-highlight"&gt;
&lt;pre class="highlight plaintext"&gt;&lt;code&gt;                                              review  sentiment
0  Stunning even for the non-gamer: This soundtra...          1
1  The best soundtrack ever to anything...                    1
2  Amazing! This soundtrack is my favorite music...           1
&lt;/code&gt;&lt;/pre&gt;

&lt;/div&gt;



&lt;p&gt;Now the dataset is finally in a format that can be used for:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Text preprocessing&lt;/li&gt;
&lt;li&gt;Feature extraction&lt;/li&gt;
&lt;li&gt;Model training&lt;/li&gt;
&lt;li&gt;Evaluation&lt;/li&gt;
&lt;/ul&gt;

&lt;h3&gt;
  
  
  What I Learned:
&lt;/h3&gt;

&lt;ul&gt;
&lt;li&gt;Not every Kaggle dataset comes as a CSV file.&lt;/li&gt;
&lt;li&gt;
&lt;code&gt;.bz2&lt;/code&gt; is simply a compressed file format.&lt;/li&gt;
&lt;li&gt;Python's built-in bz2 library can read these files directly.&lt;/li&gt;
&lt;li&gt;Amazon Review datasets use text labels instead of numerical labels.&lt;/li&gt;
&lt;li&gt;Converting the data into a DataFrame makes the next NLP steps much easier.&lt;/li&gt;
&lt;/ul&gt;

&lt;h3&gt;
  
  
  Final Thoughts
&lt;/h3&gt;

&lt;p&gt;This was a small issue, but it completely blocked my progress for a while.&lt;br&gt;
As a beginner in NLP, I am discovering that many challenges are not about machine learning algorithms but about understanding datasets and data formats.&lt;/p&gt;

&lt;p&gt;Hopefully, this saves someone else a few hours of confusion.&lt;/p&gt;

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