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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A content-based recommender system that recommends movies similar to the movie the user likes and analyses the sentiments of the reviews given by the user
| Date | Stars |
|---|---|
| 2026-07-24 | 608 |
| 2026-07-25 | 608 |
| 2026-07-28 | 608 |
| 2026-07-30 | 608 |
| 2026-08-06 | 608 |
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# Content-Based-Movie-Recommender-System-with-sentiment-analysis-using-AJAX     **Updated version of this application can be found at:** https://github.com/kishan0725/The-Movie-Cinema Content Based Recommender System recommends movies similar to the movie user likes and analyses the sentiments on the reviews given by the user for that movie. The details of the movies(title, genre, runtime, rating, poster, etc) are fetched using an API by TMDB, https://www.themoviedb.org/documentation/api, and using the IMDB id of the movie in the API, I did web scraping to get the reviews given by the user in the IMDB site using `beautifulsoup4` and performed sentiment analysis on those reviews. Link to youtube demo: https://www.youtube.com/watch?v=dhVePtyECFw ## The Movie Cinema I've developed a similar application called "The Movie Cinema" which supports all language movies. But the only thing that differs from this application is that I've used the TMDB's recommendation engine in "The Movie Cinema". The recommendation part developed by me in this application doesn't support for multi-language movies as it consumes 200% of RAM (even after deploying it to Heroku) for generating Count Vectorizer matrix for all the 700,000+ movies in the TMDB. Link to "The Movie Cinema" application: [https://tmc.kishanlal.dev/](https://tmc.kishanlal.dev/) If you can't find the movie you're searching for through auto-suggestions while typing, there's no need to worry. Simply type the name of the movie and press "enter". Even if you make some typos, it should still work fine. Source Code: https://github.com/kishan0725/The-Movie-Cinema ## Featured in Krish's Live Session on YouTube [](https://www.youtube.com/watch?v=A_78fGgQMjM) ## How to get the API key? Create an account in https://www.themoviedb.org/, click on the `API` link from the left hand sidebar in your account settings and fill all the details to apply for API key. If you are asked for the website URL, just give "NA" if you don't have one. You will see the API key in your `API` sidebar once your request is approved. ## How to run the project? 1. Clone or download this repository to your local machine. 2. Install all the libraries mentioned in the [requirements.txt](https://github.com/kishan0725/Movie-Recommendation-System-with-Sentiment-Analysis/blob/master/requirements.txt) file with the command `pip install -r requirements.txt` 3. Get your API key from https://www.themoviedb.org/. (Refer the above section on how to get the API key) 3. Replace YOUR_API_KEY in **both** the places (line no. 15 and 29) of `static/recommend.js` file and hit save. 4. Open your terminal/command prompt from your project directory and run the file `main.py` by executing the command `python main.py`. 5. Go to your browser and type `http://127.0.0.1:5000/` in the address bar. 6. Hurray! That's it. ## Architecture  ## Similarity Score : How does it decide which item is most similar to the item user likes? Here come the similarity scores. It is a numerical value ranges between zero to one which helps to determine how much two items are similar to each other on a scale of zero to one. This similarity score is obtained measuring the similarity between the text details of both of the items. So, similarity score is the measure of similarity between given text details of two items. This can be done by cosine-similarity. ## How Cosine Similarity works? Cosine similarity is a metric used to measure how sim
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matched fp:f18bccae3b92021e, topic:nlp, topic:sentiment-analysis, name:sentiment analysis