every pair of features being classified is independent of each other. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. More about Cosine Similarity : Understanding the Math behind Cosine Similarity Sentiment Analysis using Naive Bayes Algorithm The smaller the angle, higher the cosine similarity. The cosine similarity is advantageous because even if the two similar documents are far apart by the Euclidean distance (due to the size of the document), chances are they may still be oriented closer together. Mathematically, it measures the cosine of the angle between two vectors projected in a multi-dimensional space. How Cosine Similarity works?Ĭosine similarity is a metric used to measure how similar the documents are irrespective of their size. So, similarity score is the measure of similarity between given text details of two items. This similarity score is obtained measuring the similarity between the text details of both of the items. 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. How does it decide which item is most similar to the item user likes? Here come the similarity scores. But the only thing that differs from this application is that I’ve used the TMDB’s recommendation engine in “The Movie Cinema”. I’ve developed a similar movie streaming application called “Fuboo” which supports all language movies. The details of the movies(title, genre, runtime, rating, poster, etc) are fetched using an API by TMDB,, 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. So, on Twitter I may not have found what I was looking for, but at least I got to start exploring new ideas.Content-Based-Movie-Recommender-System-with-sentiment-analysis-using-AJAXĬontent 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. I tried my luck searching for movie recommendations on Twitter, and I got sidetracked when I came across this smart question: “Bollywood movie recommendation that shows women in a positive light?”. Please type one of your favorite English movies released from. Then you have “tweetbots”, production companies pretending they are movie goers raving about the latest release… The list goes on. This App will recommend mainly English movies based on user input, like Netflix or YouTube. Sure, you can go, say, to Twitter and either ask for movie recommendations or do a search – but how many of these recommendations will turn out to be sponsored posts?Īlso, what about REALLY BAD movie recommendations? People who don’t know you very well will come up with random titles that will probably end up annoying you. This type of question can only be answered by a community made of real people – no machine can be sophisticated enough to present a number of educated film options. This type of question can only be answered by a community made of real people.
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