Well that sounded like a lot of technical information that may be new or difficult to the learner. Figure 1 shows three 3-dimensional vectors and the angles between each pair. The basic concept would be to count the terms in every document and calculate the dot product of the term vectors. In vector space model, each words would be treated as dimension and each word would be independent and orthogonal to each other. Figure 1. With this in mind, we can define cosine similarity between two vectors as follows: In Java, you can use Lucene (if your collection is pretty large) or LingPipe to do this. s2 = "This sentence is similar to a foo bar sentence ." The cosine similarity is advantageous because even if the two similar documents are far apart by the Euclidean distance because of the size (like, the word ‘cricket’ appeared 50 times in one document and 10 times in another) they could still have a smaller angle between them. 2. Semantic Textual Similarity¶. The intuition behind cosine similarity is relatively straight forward, we simply use the cosine of the angle between the two vectors to quantify how similar two documents are. These algorithms create a vector for each word and the cosine similarity among them represents semantic similarity among the words. The greater the value of θ, the less the value of cos θ, thus the less the similarity between two documents. The similarity is: 0.839574928046 s1 = "This is a foo bar sentence ." Without importing external libraries, are that any ways to calculate cosine similarity between 2 strings? From trigonometry we know that the Cos(0) = 1, Cos(90) = 0, and that 0 <= Cos(θ) <= 1. In text analysis, each vector can represent a document. Cosine Similarity tends to determine how similar two words or sentence are, It can be used for Sentiment Analysis, Text Comparison and being used by lot of popular packages out there like word2vec. Once you have sentence embeddings computed, you usually want to compare them to each other.Here, I show you how you can compute the cosine similarity between embeddings, for example, to measure the semantic similarity of two texts. The cosine similarity is the cosine of the angle between two vectors. Calculate the cosine similarity: (4) / (2.2360679775*2.2360679775) = 0.80 (80% similarity between the sentences in both document) Let’s explore another application where cosine similarity can be utilised to determine a similarity measurement bteween two objects. Cosine similarity is a metric, helpful in determining, how similar the data objects are irrespective of their size. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space.It is defined to equal the cosine of the angle between them, which is also the same as the inner product of the same vectors normalized to both have length 1. We can measure the similarity between two sentences in Python using Cosine Similarity. Cosine Similarity (Overview) Cosine similarity is a measure of similarity between two non-zero vectors. Cosine Similarity. Questions: From Python: tf-idf-cosine: to find document similarity , it is possible to calculate document similarity using tf-idf cosine. In the case of the average vectors among the sentences. A good starting point for knowing more about these methods is this paper: How Well Sentence Embeddings Capture Meaning . Calculate cosine similarity of two sentence sen_1_words = [w for w in sen_1.split() if w in model.vocab] sen_2_words = [w for w in sen_2.split() if w in model.vocab] sim = model.n_similarity(sen_1_words, sen_2_words) print(sim) Firstly, we split a sentence into a word list, then compute their cosine similarity. Generally a cosine similarity between two documents is used as a similarity measure of documents. 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