Engineeering & IT Projects and Resources: Sentiment Analysis on Twitter Data

Sentiment Analysis on Twitter Data

1. Introduction

Objective: Perform sentiment analysis on Twitter data to classify tweets as positive, negative, or neutral.
Purpose: Provide insights into public opinion and sentiments for various topics or events.

2. Project Workflow

1. Problem Definition:
   - Analyze Twitter data to detect sentiment polarity.
   - Key questions:
     - What is the general sentiment about a specific topic or event?
     - Which tweets show strong positive or negative sentiments?
2. Data Collection:
   - Source: Twitter API or publicly available datasets.
   - Example: A dataset containing `Tweet Text`, `Username`, and `Timestamp`.
3. Data Preprocessing:
   - Clean and preprocess text data.
   - Remove stopwords, hashtags, and perform tokenization.
4. Sentiment Analysis:
   - Use Natural Language Processing (NLP) techniques for sentiment classification.
5. Insights and Visualization:
   - Visualize sentiment distribution and trends over time.

3. Technical Requirements

- Programming Language: Python
- Libraries/Tools:
- Data Handling: Pandas, NumPy
- NLP: NLTK, SpaCy, TextBlob
- Visualization: Matplotlib, Seaborn, WordCloud
- Sentiment Analysis: Scikit-learn, Transformers
- Twitter Data Collection: Tweepy

4. Implementation Steps

Step 1: Setup Environment

Install required libraries:
```
pip install pandas numpy matplotlib seaborn nltk tweepy textblob wordcloud
```

Step 2: Collect and Load Twitter Data

Authenticate and fetch tweets using Tweepy:
```
import tweepy

consumer_key = 'your_consumer_key'
consumer_secret = 'your_consumer_secret'
access_token = 'your_access_token'
access_token_secret = 'your_access_token_secret'

auth = tweepy.OAuth1UserHandler(consumer_key, consumer_secret, access_token, access_token_secret)
api = tweepy.API(auth)

tweets = api.search_tweets(q="Python", lang="en", count=100)
tweet_data = [{"text": tweet.text, "created_at": tweet.created_at} for tweet in tweets]
```
Load the data into a DataFrame:
```
import pandas as pd

df = pd.DataFrame(tweet_data)
```

Step 3: Text Preprocessing

Clean and preprocess the text data:
```
import re
from nltk.corpus import stopwords

nltk.download('stopwords')
stop_words = set(stopwords.words('english'))

def preprocess_text(text):
    text = re.sub(r"http\S+", "", text) # Remove URLs
    text = re.sub(r"@\w+", "", text)    # Remove mentions
    text = re.sub(r"#", "", text)       # Remove hashtags
    text = re.sub(r"[^\w\s]", "", text) # Remove punctuation
    text = text.lower()                 # Convert to lowercase
    text = " ".join([word for word in text.split() if word not in stop_words])
    return text

df['cleaned_text'] = df['text'].apply(preprocess_text)
```

Step 4: Sentiment Analysis

Perform sentiment analysis using TextBlob:
```
from textblob import TextBlob

def analyze_sentiment(text):
    polarity = TextBlob(text).sentiment.polarity
    if polarity > 0:
        return 'Positive'
    elif polarity < 0:
        return 'Negative'
    else:
        return 'Neutral'

df['sentiment'] = df['cleaned_text'].apply(analyze_sentiment)
```

Step 5: Visualize Sentiment Distribution

Plot sentiment distribution:
```
import seaborn as sns
import matplotlib.pyplot as plt

sns.countplot(x='sentiment', data=df)
plt.title("Sentiment Distribution")
plt.show()
```
Generate a word cloud for positive tweets:
```
from wordcloud import WordCloud

positive_tweets = " ".join(df[df['sentiment'] == 'Positive']['cleaned_text'])
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(positive_tweets)

plt.figure(figsize=(10, 5))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()
```

5. Expected Outcomes

1. Classification of tweets into positive, negative, and neutral sentiments.
2. Visual representation of sentiment trends.
3. Insights into public opinion for targeted topics or events.

6. Additional Suggestions

- Advanced Techniques:
- Use pre-trained language models (e.g., BERT, GPT) for better accuracy.
- Perform topic modeling using LDA for deeper insights.
- Real-time Analysis:
- Develop a real-time sentiment dashboard using Streamlit.
- Expand Dataset:
- Combine Twitter data with data from other platforms (e.g., Reddit, Facebook).

Pages