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Cosine Similarity

#artificialintelligence

Cosine Similarity measures the cosine of the angle between two non-zero vectors of an inner product space. This similarity measurement is particularly concerned with orientation, rather than magnitude. In short, two cosine vectors that are aligned in the same orientation will have a similarity measurement of 1, whereas two vectors aligned perpendicularly will have a similarity of 0. If two vectors are diametrically opposed, meaning they are oriented in exactly opposite directions (i.e. Often, however, Cosine Similarity is used in positive space, between the bounds 0 and 1. Cosine Similarity is not concerned, and does not measure, differences is magnitude (length), and is only a representation of similarities in orientation.


Compare documents similarity using Python

#artificialintelligence

In this post we are going to build a web application which will compare the similarity between two documents. We will learn the very basics of natural language processing (NLP) which is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. Let's start with the base structure of program but then we will add graphical interface to making the program much easier to use. Feel free to contribute this project in my GitHub. Natural language toolkit (NLTK) is the most popular library for natural language processing (NLP) which was written in Python and has a big community behind it.


A Case Study to Reveal if an Area of Interest has a Trend in Ongoing Tweets Using Word and Sentence Embeddings

arXiv.org Artificial Intelligence

In the field of Natural Language Processing, information extraction from texts has been the objective of many researchers for years. Many different techniques have been applied in order to reveal the opinion that a tweet might have, thus understanding the sentiment of the small writing up to 280 characters. Other than figuring out the sentiment of a tweet, a study can also focus on finding the correlation of the tweets with a certain area of interest, which constitutes the purpose of this study. In order to reveal if an area of interest has a trend in ongoing tweets, we have proposed an easily applicable automated methodology in which the Daily Mean Similarity Scores that show the similarity between the daily tweet corpus and the target words representing our area of interest is calculated by using a na\"ive correlation-based technique without training any Machine Learning Model. The Daily Mean Similarity Scores have mainly based on cosine similarity and word/sentence embeddings computed by Multilanguage Universal Sentence Encoder and showed main opinion stream of the tweets with respect to a certain area of interest, which proves that an ongoing trend of a specific subject on Twitter can easily be captured in almost real time by using the proposed methodology in this study. We have also compared the effectiveness of using word versus sentence embeddings while applying our methodology and realized that both give almost the same results, whereas using word embeddings requires less computational time than sentence embeddings, thus being more effective. This paper will start with an introduction followed by the background information about the basics, then continue with the explanation of the proposed methodology and later on finish by interpreting the results and concluding the findings.


Calculate Cosine Similarity Using Scipy – Data Sets & Sample Code

@machinelearnbot

Cosine Similarity is a measure of similarity between two vectors that calculates the cosine of the angle between them. We have shared data sets, sample code & an example case study in implementing Cosine Similarity. We are looking to find a place to settle down in California. We like a place called Montecito, CA and want to find similar towns & cities to look for places. How would we go about doing it?


Calculate Cosine Similarity Using Scipy – Data Sets & Sample Code

@machinelearnbot

Cosine Similarity is a measure of similarity between two vectors that calculates the cosine of the angle between them. We have shared data sets, sample code & an example case study in implementing Cosine Similarity. We are looking to find a place to settle down in California. We like a place called Montecito, CA and want to find similar towns & cities to look for places. How would we go about doing it?