Developing a Sentiment Analysis System with Python

In this post, we will learn how to develop a sentiment analysis system using Python. Sentiment analysis is a natural language processing technique that helps determine the sentiment or emotion behind a piece of text. Let's get started!

Installing Required Libraries

First, install the required libraries by running the following command in your terminal:

pip install numpy pandas sklearn textblob

Loading the Dataset

We will use a sample dataset containing movie reviews and their sentiments. You can download it or use any other dataset of your choice. To load the dataset, we'll use the pandas library:

import pandas as pd
data = pd.read_csv('movie_reviews.csv')

Preparing the Data

Next, we need to preprocess the text data by converting it to lowercase, removing special characters, and stemming the words. We'll define a function for this purpose:

import re
from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize

def preprocess(text):
    text = text.lower()
    text = re.sub(r'[^a-z]+', ' ', text)
    stemmer = PorterStemmer()
    text = ' '.join([stemmer.stem(word) for word in word_tokenize(text)])
    return text

data['review'] = data['review'].apply(preprocess)

Training the Model

Now, we'll split the data into training and testing sets and train a sentiment analysis model using TextBlob:

from sklearn.model_selection import train_test_split
from textblob import TextBlob

X_train, X_test, y_train, y_test = train_test_split(data['review'], data['sentiment'], test_size=0.2)

def predict_sentiment(text):
    return 'positive' if TextBlob(text).sentiment.polarity > 0 else 'negative'

y_pred = X_test.apply(predict_sentiment)

Evaluating the Model

Finally, we'll evaluate the performance of our sentiment analysis model using accuracy score:

from sklearn.metrics import accuracy_score
print("Accuracy:", accuracy_score(y_test, y_pred))

Conclusion

In this post, we have learned how to develop a sentiment analysis system using Python. We have covered data preprocessing, training the model, and evaluating its performance. With this knowledge, you can now create your own sentiment analysis system and use it for various applications such as social media monitoring, customer feedback analysis, and more. Happy coding!