Project 1: Analyzing Customer Reviews
1.4: Basic Sentiment Analysis
At this point in your data analysis journey, you have already gone through the crucial steps of collecting and cleaning your data, as well as creating insightful visualizations to help you better understand patterns and trends.
However, there is still one more important step left to take before you can draw meaningful conclusions from your data: sentiment analysis. This final step is particularly important as it allows you to uncover the emotional tone hidden within the reviews and feedback you have collected. By applying sentiment analysis techniques, you can effectively classify reviews as positive, neutral, or negative based solely on their textual content.
This will not only help you gain a deeper understanding of your data but also enable you to make more informed decisions and take relevant actions based on the insights you have uncovered. So, let's dive into the exciting world of sentiment analysis and learn how to unlock the full potential of your data!
1.4.1 TextBlob for Sentiment Analysis
For this example, let's use the TextBlob library, which is excellent for beginners. It will give us two properties: polarity and subjectivity for each review.
First, make sure you install TextBlob:
pip install textblobThen, proceed with the analysis:
from textblob import TextBlob
# Function to apply sentiment analysis
def analyze_sentiment(text):
analysis = TextBlob(text)
if analysis.sentiment.polarity > 0:
return 'Positive'
elif analysis.sentiment.polarity == 0:
return 'Neutral'
else:
return 'Negative'
reviews['text_blob_sentiment'] = reviews['cleaned_review_text'].apply(analyze_sentiment)
# Display some reviews and their sentiment
print(reviews[['cleaned_review_text', 'text_blob_sentiment']].head())1.4.2 Visualizing TextBlob Results
Now, let's visualize the distribution of sentiments as analyzed by TextBlob.
sns.countplot(x='text_blob_sentiment', data=reviews, order=['Positive', 'Neutral', 'Negative'])
plt.title('Distribution of Sentiments - TextBlob')
plt.xlabel('Sentiment')
plt.ylabel('Number of Reviews')
plt.show()1.4.3 Comparing TextBlob Sentiments with Ratings
It might be interesting to compare the sentiment analysis results from TextBlob with the sentiments we categorized based on ratings.
# Creating a crosstab
sentiment_comparison = pd.crosstab(reviews['sentiment'], reviews['text_blob_sentiment'])
print(sentiment_comparison)This will help you assess how well the TextBlob sentiment analysis aligns with the actual ratings given by users.
And there you have it! You've successfully traversed the entire journey—from data collection to sentiment analysis—in Project 1. Reflect on what you've learned. Is the sentiment generally positive, negative, or neutral? How does this sentiment data corroborate or contradict the numerical ratings or your earlier visualizations?
Remember, data analysis is as much about asking the right questions as it is about finding answers. Your newfound skills in Python, pandas, matplotlib, seaborn, and TextBlob have equipped you to continue your journey in data science with confidence.
See you in the next chapter, dear reader. Until then, may your data always be clean, and your graphs ever insightful!
1.4: Basic Sentiment Analysis
At this point in your data analysis journey, you have already gone through the crucial steps of collecting and cleaning your data, as well as creating insightful visualizations to help you better understand patterns and trends.
However, there is still one more important step left to take before you can draw meaningful conclusions from your data: sentiment analysis. This final step is particularly important as it allows you to uncover the emotional tone hidden within the reviews and feedback you have collected. By applying sentiment analysis techniques, you can effectively classify reviews as positive, neutral, or negative based solely on their textual content.
This will not only help you gain a deeper understanding of your data but also enable you to make more informed decisions and take relevant actions based on the insights you have uncovered. So, let's dive into the exciting world of sentiment analysis and learn how to unlock the full potential of your data!
1.4.1 TextBlob for Sentiment Analysis
For this example, let's use the TextBlob library, which is excellent for beginners. It will give us two properties: polarity and subjectivity for each review.
First, make sure you install TextBlob:
pip install textblobThen, proceed with the analysis:
from textblob import TextBlob
# Function to apply sentiment analysis
def analyze_sentiment(text):
analysis = TextBlob(text)
if analysis.sentiment.polarity > 0:
return 'Positive'
elif analysis.sentiment.polarity == 0:
return 'Neutral'
else:
return 'Negative'
reviews['text_blob_sentiment'] = reviews['cleaned_review_text'].apply(analyze_sentiment)
# Display some reviews and their sentiment
print(reviews[['cleaned_review_text', 'text_blob_sentiment']].head())1.4.2 Visualizing TextBlob Results
Now, let's visualize the distribution of sentiments as analyzed by TextBlob.
sns.countplot(x='text_blob_sentiment', data=reviews, order=['Positive', 'Neutral', 'Negative'])
plt.title('Distribution of Sentiments - TextBlob')
plt.xlabel('Sentiment')
plt.ylabel('Number of Reviews')
plt.show()1.4.3 Comparing TextBlob Sentiments with Ratings
It might be interesting to compare the sentiment analysis results from TextBlob with the sentiments we categorized based on ratings.
# Creating a crosstab
sentiment_comparison = pd.crosstab(reviews['sentiment'], reviews['text_blob_sentiment'])
print(sentiment_comparison)This will help you assess how well the TextBlob sentiment analysis aligns with the actual ratings given by users.
And there you have it! You've successfully traversed the entire journey—from data collection to sentiment analysis—in Project 1. Reflect on what you've learned. Is the sentiment generally positive, negative, or neutral? How does this sentiment data corroborate or contradict the numerical ratings or your earlier visualizations?
Remember, data analysis is as much about asking the right questions as it is about finding answers. Your newfound skills in Python, pandas, matplotlib, seaborn, and TextBlob have equipped you to continue your journey in data science with confidence.
See you in the next chapter, dear reader. Until then, may your data always be clean, and your graphs ever insightful!
1.4: Basic Sentiment Analysis
At this point in your data analysis journey, you have already gone through the crucial steps of collecting and cleaning your data, as well as creating insightful visualizations to help you better understand patterns and trends.
However, there is still one more important step left to take before you can draw meaningful conclusions from your data: sentiment analysis. This final step is particularly important as it allows you to uncover the emotional tone hidden within the reviews and feedback you have collected. By applying sentiment analysis techniques, you can effectively classify reviews as positive, neutral, or negative based solely on their textual content.
This will not only help you gain a deeper understanding of your data but also enable you to make more informed decisions and take relevant actions based on the insights you have uncovered. So, let's dive into the exciting world of sentiment analysis and learn how to unlock the full potential of your data!
1.4.1 TextBlob for Sentiment Analysis
For this example, let's use the TextBlob library, which is excellent for beginners. It will give us two properties: polarity and subjectivity for each review.
First, make sure you install TextBlob:
pip install textblobThen, proceed with the analysis:
from textblob import TextBlob
# Function to apply sentiment analysis
def analyze_sentiment(text):
analysis = TextBlob(text)
if analysis.sentiment.polarity > 0:
return 'Positive'
elif analysis.sentiment.polarity == 0:
return 'Neutral'
else:
return 'Negative'
reviews['text_blob_sentiment'] = reviews['cleaned_review_text'].apply(analyze_sentiment)
# Display some reviews and their sentiment
print(reviews[['cleaned_review_text', 'text_blob_sentiment']].head())1.4.2 Visualizing TextBlob Results
Now, let's visualize the distribution of sentiments as analyzed by TextBlob.
sns.countplot(x='text_blob_sentiment', data=reviews, order=['Positive', 'Neutral', 'Negative'])
plt.title('Distribution of Sentiments - TextBlob')
plt.xlabel('Sentiment')
plt.ylabel('Number of Reviews')
plt.show()1.4.3 Comparing TextBlob Sentiments with Ratings
It might be interesting to compare the sentiment analysis results from TextBlob with the sentiments we categorized based on ratings.
# Creating a crosstab
sentiment_comparison = pd.crosstab(reviews['sentiment'], reviews['text_blob_sentiment'])
print(sentiment_comparison)This will help you assess how well the TextBlob sentiment analysis aligns with the actual ratings given by users.
And there you have it! You've successfully traversed the entire journey—from data collection to sentiment analysis—in Project 1. Reflect on what you've learned. Is the sentiment generally positive, negative, or neutral? How does this sentiment data corroborate or contradict the numerical ratings or your earlier visualizations?
Remember, data analysis is as much about asking the right questions as it is about finding answers. Your newfound skills in Python, pandas, matplotlib, seaborn, and TextBlob have equipped you to continue your journey in data science with confidence.
See you in the next chapter, dear reader. Until then, may your data always be clean, and your graphs ever insightful!
1.4: Basic Sentiment Analysis
At this point in your data analysis journey, you have already gone through the crucial steps of collecting and cleaning your data, as well as creating insightful visualizations to help you better understand patterns and trends.
However, there is still one more important step left to take before you can draw meaningful conclusions from your data: sentiment analysis. This final step is particularly important as it allows you to uncover the emotional tone hidden within the reviews and feedback you have collected. By applying sentiment analysis techniques, you can effectively classify reviews as positive, neutral, or negative based solely on their textual content.
This will not only help you gain a deeper understanding of your data but also enable you to make more informed decisions and take relevant actions based on the insights you have uncovered. So, let's dive into the exciting world of sentiment analysis and learn how to unlock the full potential of your data!
1.4.1 TextBlob for Sentiment Analysis
For this example, let's use the TextBlob library, which is excellent for beginners. It will give us two properties: polarity and subjectivity for each review.
First, make sure you install TextBlob:
pip install textblobThen, proceed with the analysis:
from textblob import TextBlob
# Function to apply sentiment analysis
def analyze_sentiment(text):
analysis = TextBlob(text)
if analysis.sentiment.polarity > 0:
return 'Positive'
elif analysis.sentiment.polarity == 0:
return 'Neutral'
else:
return 'Negative'
reviews['text_blob_sentiment'] = reviews['cleaned_review_text'].apply(analyze_sentiment)
# Display some reviews and their sentiment
print(reviews[['cleaned_review_text', 'text_blob_sentiment']].head())1.4.2 Visualizing TextBlob Results
Now, let's visualize the distribution of sentiments as analyzed by TextBlob.
sns.countplot(x='text_blob_sentiment', data=reviews, order=['Positive', 'Neutral', 'Negative'])
plt.title('Distribution of Sentiments - TextBlob')
plt.xlabel('Sentiment')
plt.ylabel('Number of Reviews')
plt.show()1.4.3 Comparing TextBlob Sentiments with Ratings
It might be interesting to compare the sentiment analysis results from TextBlob with the sentiments we categorized based on ratings.
# Creating a crosstab
sentiment_comparison = pd.crosstab(reviews['sentiment'], reviews['text_blob_sentiment'])
print(sentiment_comparison)This will help you assess how well the TextBlob sentiment analysis aligns with the actual ratings given by users.
And there you have it! You've successfully traversed the entire journey—from data collection to sentiment analysis—in Project 1. Reflect on what you've learned. Is the sentiment generally positive, negative, or neutral? How does this sentiment data corroborate or contradict the numerical ratings or your earlier visualizations?
Remember, data analysis is as much about asking the right questions as it is about finding answers. Your newfound skills in Python, pandas, matplotlib, seaborn, and TextBlob have equipped you to continue your journey in data science with confidence.
See you in the next chapter, dear reader. Until then, may your data always be clean, and your graphs ever insightful!