IMDB Scraper¶

from sentimatrix import Sentimatrix

async with Sentimatrix() as sm:
    reviews = await sm.scrape_reviews(
        "https://www.imdb.com/title/tt0111161/",  # Shawshank Redemption
        platform="imdb",
        max_reviews=100
    )

    for review in reviews:
        print(f"Rating: {review.rating}/10")
        print(f"Title: {review.title}")
        print(f"Review: {review.text[:200]}...")

reviews = await sm.scrape_reviews(
    url="https://imdb.com/title/tt.../",
    platform="imdb",
    max_reviews=200,           # Max reviews
    sort_by="helpfulness",     # "helpfulness", "date", "rating"
    filter_rating=None,        # Filter by star rating (1-10)
)

# Movie/TV show page
"https://www.imdb.com/title/tt0111161/"

# Reviews page
"https://www.imdb.com/title/tt0111161/reviews"

# Just IMDB ID
"tt0111161"

@dataclass
class Review:
    text: str                    # Review content
    title: str                   # Review title
    author: str                  # Reviewer username
    rating: int                  # 1-10 stars
    posted_date: datetime        # Review date
    helpful_count: int           # Helpful votes
    platform: str = "imdb"
    metadata: dict               # Extra data

review.metadata = {
    "imdb_id": "tt0111161",
    "movie_title": "The Shawshank Redemption",
    "movie_year": 1994,
    "review_id": "rw1234567",
    "spoiler_warning": False,
    "total_votes": 150
}

async with Sentimatrix(config) as sm:
    # Scrape reviews
    reviews = await sm.scrape_reviews(
        "tt0111161",  # Shawshank Redemption
        platform="imdb",
        max_reviews=500
    )

    # Analyze sentiment
    results = await sm.analyze_batch([r.text for r in reviews])

    # Correlate with ratings
    for review, result in zip(reviews, results):
        print(f"Rating: {review.rating}/10, Sentiment: {result.sentiment}")

    # Generate summary
    summary = await sm.summarize_reviews(reviews)
    print(summary)