Sentiment Analysis Model (NLP) - Hands-On Project

Overview

Sentiment analysis is a Natural Language Processing (NLP) technique used to determine the sentiment expressed in textual data. This hands-on project will guide you step-by-step through collecting data, preprocessing text, training machine learning models, evaluating their performance, and visualizing sentiment trends. By the end of this project, you will have a fully functional sentiment analysis pipeline that can classify text as positive, negative, or neutral.

1. Applications of Sentiment Analysis

Real-World Use Cases

Customer Feedback Analysis: Assess product reviews to improve business strategies.
Social Media Monitoring: Track brand reputation and customer sentiment.
Market Research: Identify trends and public opinion on new products or policies.
Financial Sentiment Analysis: Predict stock market movements based on news and reports.

📌 Example: A company uses sentiment analysis to analyze Amazon product reviews and adjust marketing strategies based on customer feedback.

2. Data Collection and Preprocessing

Step 1: Collecting the Dataset

For publicly available datasets, explore the following sources:

IMDb Reviews Dataset – Movie review dataset for sentiment classification.
Amazon Reviews Dataset – Customer feedback dataset from Amazon.
Twitter Sentiment Analysis Dataset – Large-scale tweet sentiment dataset.
UCI Sentiment Analysis Dataset – Labeled sentiment data from various domains.

Task: Choose a Data Source

You can use any of the following methods to collect sentiment data:

Download pre-existing datasets (IMDb, Amazon Reviews, Twitter Sentiment Dataset)
Scrape product reviews using BeautifulSoup or Scrapy
Fetch live tweets using the Twitter API

Code: Scraping Tweets using Tweepy API

import tweepy
import pandas as pd

# Twitter API credentials
API_KEY = "your_api_key"
API_SECRET = "your_api_secret"
ACCESS_TOKEN = "your_access_token"
ACCESS_SECRET = "your_access_secret"

# Authenticate API
auth = tweepy.OAuthHandler(API_KEY, API_SECRET)
auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)
api = tweepy.API(auth)

# Fetch tweets
def get_tweets(query, count=100):
    tweets = tweepy.Cursor(api.search_tweets, q=query, lang="en", tweet_mode="extended").items(count)
    return [tweet.full_text for tweet in tweets]

# Example usage
data = get_tweets("AI technology", count=200)
df = pd.DataFrame(data, columns=["text"])
df.to_csv("tweets.csv", index=False)

📌 Task: Modify the script to extract tweets for a specific brand and analyze its reputation.

Step 2: Data Preprocessing

Task: Clean and Prepare Text Data

Text preprocessing is crucial to remove unwanted elements and standardize text. Steps include:

Removing special characters and numbers
Tokenizing sentences into words
Removing stopwords
Lemmatizing words to their root forms

Code: Preprocessing Text Data

import re
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer

nltk.download('stopwords')
nltk.download('punkt')
nltk.download('wordnet')

def preprocess_text(text):
    text = text.lower()
    text = re.sub(r'[^a-zA-Z0-9]', ' ', text)
    tokens = word_tokenize(text)
    tokens = [word for word in tokens if word not in stopwords.words('english')]
    lemmatizer = WordNetLemmatizer()
    tokens = [lemmatizer.lemmatize(word) for word in tokens]
    return ' '.join(tokens)

sample_text = "I absolutely loved this product! It's amazing."
print(preprocess_text(sample_text))

📌 Task: Implement a function to remove URLs and user mentions from tweets.

3. Model Training and Evaluation

Step 1: Feature Extraction

Feature extraction transforms textual data into numerical representations suitable for machine learning models. Several methods can be used depending on the complexity of the model and the amount of contextual information needed:

Bag-of-Words (BoW):

Represents text as a sparse matrix where each column corresponds to a unique word, and the values indicate word frequency.
Simple but does not capture word order or context.

TF-IDF (Term Frequency-Inverse Document Frequency):

Assigns a weight to each word based on its frequency within a document relative to the entire corpus.
Reduces the influence of common words while emphasizing more informative terms.

Word Embeddings (Word2Vec, GloVe, FastText):

Maps words into dense vector spaces, capturing semantic meaning and word relationships.
More effective for deep learning models that require contextual understanding.

Transformer-Based Embeddings (BERT, GPT, T5, etc.):

Uses deep contextualized representations that take into account the meaning of a word based on its surrounding context.
Best suited for advanced NLP tasks requiring high-level language comprehension.

Code Example: Implementing TF-IDF Vectorization

from sklearn.feature_extraction.text import TfidfVectorizer

# Sample dataset
corpus = [
    "I love this product! It's amazing.",
    "Terrible experience, not recommended.",
    "Decent quality, but could be better."
]

# Initialize TF-IDF Vectorizer
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)

# Display feature names and transformed matrix
print("Feature Names:", vectorizer.get_feature_names_out())
print("TF-IDF Matrix:
", X.toarray())

📌 Task: Implement Word2Vec or FastText embeddings for feature extraction and compare the results with TF-IDF.

Task: Convert Text to Numerical Representation

Use TF-IDF (Term Frequency-Inverse Document Frequency) to weigh words based on their importance.
Use Word2Vec, GloVe, or BERT embeddings for better contextual understanding.

Step 2: Training the Model

Train a Naïve Bayes classifier as a simple baseline.
Train a Logistic Regression model for a stronger benchmark.
Train an LSTM model for improved deep learning performance.

Code: Training a Logistic Regression Model

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import pandas as pd

# Load dataset
df = pd.read_csv("tweets.csv")
df["text"] = df["text"].apply(preprocess_text)

# Feature extraction
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(df['text'])
y = df['sentiment']  # Assume sentiment labels are provided

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = LogisticRegression()
model.fit(X_train, y_train)

# Evaluate model
predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
print(f"Model Accuracy: {accuracy:.2f}")

📌 Task: Compare Logistic Regression with a deep learning-based model such as LSTM.

4. Sentiment Visualization and Interpretation

Step 1: Data Visualization

Task: Create Charts and Graphs to Understand Sentiments

Word Clouds: Display the most frequently used words in positive vs. negative reviews.
Confusion Matrix: Evaluate model performance.

Code: Visualizing Sentiment Distribution

import matplotlib.pyplot as plt
import seaborn as sns

sns.countplot(x=df['sentiment'])
plt.title("Sentiment Distribution")
plt.xlabel("Sentiment")
plt.ylabel("Count")
plt.show()

📌 Case Study: A company used sentiment analysis on Twitter data to track brand reputation and adjusted marketing strategies accordingly.

Conclusion

Sentiment analysis is a powerful tool for extracting insights from textual data. By preprocessing text, training models, and visualizing results, we can analyze sentiment trends effectively. This project provides a foundation for more advanced NLP applications.

✅ Key Takeaway: Sentiment analysis enables businesses to derive actionable insights from textual data, improving decision-making and customer engagement.

📌 Next Steps: The next project will focus on Chatbot Development, covering text preprocessing, model training, and deployment strategies.

TutorialsDestiny

Sentiment Analysis Model (NLP) – Hands-On Project