Sentiment Analysis with Python: A Rookie's Guide
Published: 2023-11-11 22:02:55

Understanding how people feel about a product, service, or even the newest blockbuster movie may be a game-changer in the enormous sea of internet exchanges. The key to this wizardry is sentiment analysis, the skill of extracting emotions from text. This step-by-step tutorial, which uses Python's robust NLTK and TextBlob packages, will demystify sentiment analysis regardless of your level of programming knowledge. But first of all, what is Sentiment Analysis?

## **WHAT IS SENTIMENT ANALYSIS**
Sentiment analysis focuses on the polarity of a text (positive, negative, neutral) but it also goes beyond polarity to detect specific feelings and emotions (angry, happy, sad, etc), urgency (urgent, not urgent) and even intentions (interested, very interested, not interested).

## **TYPES OF SENTIMENT ANALYSIS**
- Graded Sentiment Analysis: Expanding your polarity categories to include varying degrees of positive and negative, such (Very positive, Positive, Neutral, Negative, Very negative), might be a good idea if polarity precision is crucial to your organization.

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- Emotion Detection: Sentiment analysis with emotion identification enables you to identify emotions other than polarity, such as joy, annoyance, rage, and grief. Lexicons, or collections of words and the emotions they represent, are used by many emotion detection systems, as are sophisticated machine learning techniques.

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- Aspect Based Sentiment Analysis: A sophisticated type of sentiment analysis known as aspect-based sentiment analysis (ABSA) focuses on the particular characteristics or features that are discussed in the text rather than just classifying the general sentiment of the text. Put differently, ABSA seeks to recognize and evaluate the opinions stated on various parts or facets of a good, service, or subject.

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- Multilingual Sentiment Analysis: The technique of examining and identifying the sentiment represented in text data that spans numerous languages is known as multilingual sentiment analysis. Proficiency in many languages is essential for scholars, corporations, and organizations in the age of globalization and cross-linguistic digital communication.


You may be asking yourself how to do sentiment analysis on your own at this point, and I've got you covered. Here's how:

## **WHAT YOU'LL NEED:**
Please make sure Python is installed on your computer before continuing. If not, [python.org](url) has a download available. Once that's resolved, you should acquire NLTK and TextBlob, our journey's dependable companions for sentiment analysis. Open up your terminal or command prompt and type:

```
# This lines of code below will install the Natural Language Toolkit (NLTK) and TextBlob libraries
pip install nltk
pip install textblob

```

## **Step 1: Gather your Data**
Our initial task was to collect data for analysis. Grab a dataset with plenty of text because that's what we'll be working with. For this, Twitter is a gold mine. You can gather tweets on your preferred subject using their API.

## **Step 2: Text Processing**
Before beginning sentiment analysis, our data has to be thoroughly cleaned. This entails clearing out any unnecessary clutter and putting information in a manner that our Python tools can use.

- **Removing Punctuations:** We don't care about periods or exclamation marks.
- **Tokenization:** Splitting text into words.
- **Lowercasing:** Saying Good bye to Capital letters.
- **Remove Stopwords:** Common words that don't add much meaning

## **Step 3: NLTK for Sentiment Analysis**
The Natural Language Toolkit (NLTK) is a powerful language processing tool. It will be used to determine the tone of our writing. A good feeling is represented by a value larger than zero, and a negative sentiment is represented by a number less than zero. Welcome to the world of polarity. Neutral? It's not far from zero.

```
# Here we import the SentimentIntensityAnalyzer class from NLTK's Vader module
from nltk.sentiment.vader import SentimentIntensityAnalyzer

# Here we create an instance of the SentimentIntensityAnalyzer
sid = SentimentIntensityAnalyzer()

# Here we calculate sentiment scores for the given text using the Vader sentiment analysis tool
sentiment = sid.polarity_scores(your_text)

```
## **Step 4: TextBlob for Sentiment Analysis**
Contrarily, TextBlob is kind and uncomplicated. Furthermore, it categorizes text as positive, negative, or neutral in addition to providing us with a sentiment score.

```
# Here we import the TextBlob class from the TextBlob library
from textblob import TextBlob
analysis = TextBlob(your_text)

# We then proceed to create an instance of TextBlob with the given text
sentiment = analysis.sentiment.polarity

```
## **Step 5: Visualizing Your Sentiment Analysis Results**
Understanding sentiment scores is critical, but displaying the data offers another degree of understanding. Let's make a pie chart that shows the distribution of positive, negative, and neutral feelings in your collection using a basic example.

```
import matplotlib.pyplot as plt

# Let's assume we have a list of sentiment scores for multiple texts
sentiment_scores = [0.02, 0.06, 0.05, 0.03, 0.04, 0.08]

# Let's count the number of positive, negative, and neutral sentiments
positive_count = sum(1 for score in sentiment_scores if score > 0.05)
negative_count = sum(1 for score in sentiment_scores if score < 0.05)
neutral_count = len(sentiment_scores) - positive_count - negative_count

# Here is our data for plotting
labels = ['Positive', 'Negative', 'Neutral']
sizes = [positive_count, negative_count, neutral_count]
colors = ['green', 'red', 'gray']

# Then we plot a pie chart
plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
plt.axis('equal')  # Equal aspect ratio ensures that the pie is drawn as a circle.
plt.title('Sentiment Analysis Results')
plt.show()
```

## **Real World Applications of Sentiment Analysis: Understanding the Impact**
- Business Understanding: 

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/hvme5kkeeposc2on1au2.jpg)
Sentiment analysis changes how decisions are made in business. Businesses are able to identify areas for development, assess consumer happiness, and formulate successful strategies through the analysis of social media sentiment and reviews. Businesses are guided toward activities that favorably align with their audience by using this data-driven strategy, which elevates decision-making processes.
- Customer Feedback Analysis: 

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/6406m6vowyvgshz37du7.png)
Any corporation must understand the opinions of its clients regarding its goods and services. Businesses can efficiently sort through enormous volumes of client feedback thanks to sentiment analysis. Sentiment analysis offers actionable information from surveys, social media comments, and e-commerce platform evaluations. Negative feelings reveal areas that require attention and improvement, whereas positive sentiments can be used for marketing and showcasing strengths.
- Risk Analysis: 

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/swm4eby0r8he0ouffh49.png)
In the financial industry, sentiment analysis is a useful method for determining market sentiment. To determine how people feel about financial products, traders and investors examine textual data such as news stories, social media posts, and other texts. Making wise investing decisions, controlling risks, and keeping up with market developments are all made easier with the help of this knowledge.

## **Bonus Step: Code Snippet**
Here is a sneak peek at a small bit of Python code wizardry that uses the Natural Language Toolkit (NLTK) Library to perform sentiment analysis.

```
from textblob import TextBlob

text = input('Input Your Text: ')

analysis = TextBlob(text)
sentiment = analysis.sentiment.polarity

if sentiment > 0.05:
    print("Your Text is Positive 😄")
elif sentiment < 0.05:
    print("Your Text is Negative 😢")
else:
    print("Your Text is Neutral 😐")

```
## **Conclusion**
Sentiment analysis is a very useful tool for organizations since it makes it possible to obtain true client feedback in an impartial, less biased manner. When done correctly, it may significantly improve your systems, applications, or online initiatives. Exploring the world of sentiment analysis is made possible by utilizing TextBlob and NLTK.
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Apply your newly acquired knowledge to meaningfully interact, investigate, and apply the vast amount of textual material in order to derive insights. You now have the ability to see the feelings that are hidden behind words thanks to this symbolic magic wand.