Introduction to the Study of Sentiment in Film Reviews
In today’s digital world, reviews are central to shaping public opinion, especially regarding movies. Platforms like Rotten Tomatoes aggregate critic and audience reviews, but the alignment between critics and viewers often sparks debate. Do critics and audiences generally agree on how they feel about a film? What genres elicit the most potent emotional responses? To answer these questions, we turn to sentiment analysis, a data-driven approach to understanding emotions expressed in text.
In this blog post, we will explore how sentiment analysis can help uncover patterns in Rotten Tomatoes reviews. We will analyze sentiment across different genres and compare the views of critics and audiences. We will use AWS Comprehend to analyze scraped reviews, visualizing the relationship between sentiments and ratings. Finally, we’ll provide insights into how this analysis can inform movie selection.
Methodology: Collecting Data and Setting Up Tools
The first step in performing sentiment analysis on Rotten Tomatoes reviews is to gather the necessary data. We focused on scraping both critic and audience reviews for a variety of films across different genres. After collecting this data, we used AWS Comprehend for sentiment analysis. AWS Comprehend is a Natural Language Processing (NLP) service that uses machine learning to extract insights, including sentiment, from text.
Here’s an overview of our methodology:
- Data Collection: Scraping film reviews from Rotten Tomatoes.
- Data Cleaning: Preprocessing the reviews to remove noise (e.g., HTML tags, symbols).
- Sentiment Analysis: AWS Comprehend will determine each review’s sentiment polarity (positive, neutral, negative).
- Data Visualization: Visualizing the relationships between sentiment, ratings, and genres using Python’s data analysis libraries.
Scraping Reviews and Extracting Ratings from Rotten Tomatoes
To collect our dataset, we used Python’s BeautifulSoup and Requests libraries to scrape Rotten Tomatoes reviews. The reviews were gathered for movies spanning various genres (e.g., action, drama, comedy, horror). We extracted critic and audience reviews separately, along with the corresponding ratings.
- Review Scraping: Each movie page on Rotten Tomatoes contains reviews and scores from critics and viewers. We automated the process of scraping reviews using BeautifulSoup.
- Rating Extraction: For each review, we captured the associated rating (typically out of 10 or as a percentage) along with the reviewer’s name, review date, and whether the review came from a critic or an audience member.
This raw data served as the foundation for conducting sentiment analysis.
Conducting Sentiment Analysis Using AWS Comprehend
Once the data was collected and cleaned, we integrated AWS Comprehend to perform the sentiment analysis. AWS Comprehend’s sentiment detection identifies whether the overall sentiment of a review is positive, negative, neutral, or mixed.
Steps to Conduct Sentiment Analysis with AWS Comprehend:
- Text Input: Reviews are sent to AWS Comprehend’s API as text inputs.
- Sentiment Detection: For each review, AWS Comprehend returns a sentiment score (positive, negative, neutral, or mixed) and a confidence score for each sentiment class.
- Data Collection: The sentiment results are stored for further analysis.
Analyzing Sentiment Patterns Across Different Genres
One of the critical aspects of our analysis was examining how sentiment varied across film genres. By grouping the reviews based on genre (e.g., comedy, horror, drama, action), we identified exciting trends in viewer and critic reactions.
For example:
- Comedy films tend to have more positive audience sentiment compared to critical sentiment. Audiences generally appreciate humor, whereas critics may have stricter standards for evaluating comedy films.
- Horror films exhibit a split in sentiment. While many viewers report positive experiences, many express negative feelings, especially if the film does not meet their expectations for suspense or fear.
By aggregating sentiment scores across different genres, we could better understand how various types of films affect critics and viewers emotionally.
Visualizing the Relationship Between Sentiment and Ratings
After sentiment analysis, we visualized the relationship between the sentiment scores and the corresponding Rotten Tomatoes ratings (critic and audience ratings). Using Python libraries such as matplotlib and seaborn, we created scatter plots, bar graphs, and line charts to display sentiment patterns.
Critical Insights from Visualization:
- Movies with higher ratings generally exhibited positive sentiment, though there were exceptions. For example, some films with high audience ratings had mixed or negative critic sentiments.
- Critic ratings were often less influenced by emotional sentiment, suggesting a more analytical approach to film evaluation. In contrast, audience ratings showed a stronger correlation with the emotional sentiment of their reviews.
- In some cases, movies that were critically acclaimed (e.g., dramas or documentaries) had neutral or mixed audience sentiment, possibly indicating that these films were appreciated for their quality but didn’t emotionally resonate as strongly with viewers.
Summary and Recommendations for Movie Selection Based on Sentiment Analysis
Based on our analysis, sentiment analysis can be a powerful tool in understanding film reviews. It helps identify how critics and audiences align or diverge their feelings toward different genres. Additionally, it provides insights into how sentiment correlates with ratings, offering a new dimension to movie evaluation.
Recommendations for Movie Selection:
- If you prefer audience-aligned films, focus on genres like comedy or action, where sentiment and ratings are typically in sync.
- For a critical perspective, look at movies in genres like drama or documentary, where critic sentiment is often more neutral and analytical.
- Horror enthusiasts should be cautious of the mixed sentiment in reviews, especially when selecting films with polarizing reviews.
By leveraging tools like AWS Comprehend for sentiment analysis, moviegoers and industry professionals can gain deeper insights into viewer and critic reactions, ultimately making more informed decisions.