Revolutionizing Text Similarity and Clustering

The volume of text data generated in recent years has increased exponentially. It is very important for businesses to have a blueprint to dig out actionable insights from the generated text. From risk management ato social media analytics, dealing with text data has never been more essential.

In today’s blog, we are shedding light on Text Similarity and Text Clustering which can help you bring the best out of textual data.

Text Similarity is a technique which compares a text data with another and finds the similarity between them. It is about finding out the degree of closeness of given text. Handling the text brings us to Natural Language Processing where we have to use various NLP techniques to process the given raw textual data and help Text Similarity model to find out the similarity more efficiently. The only difference between text similarity and vector search is that text similarity only deals with textual data and vector search deals with all type of data.

Text Clustering is a method of grouping unlabeled texts in a way that the texts in the same group are more similar to each other than to those in other groups. These groups are known as clusters. Vector databases cluster the similar items and enable similarity searches. Text Clustering algorithms process textual data and find if natural groups exist in the data.

The Need For Revolutionizing Text Similarity and Clustering in The Modern World

The need for revolutionizing these techniques is driven by various factors, technology, and the demands of changing society.

1. Content curation
2. Explosion of data
3. Ethical considerations
4. Research
5. Language translation
6. Improved user experience
7. Multimodal data
8. Natural language processing
9. Business insight and intelligence
10. Fraud detection and security

Explaining Text Similarity and Clustering Techniques

Traditional techniques have been fundamental in NLP and data analysis. These methods have limited capabilities and because of this there’s a rise in need for advanced approaches. Here are some traditional techniques of Text Similarity and Text Clustering:

Text Similarity

Cosine similarity

Cosine similarity measures the similarity between 2 texts based on angle linking their word vectors. It is usually used with TF-IDF vectors that represent each other’s significance in a document.

This technique finds similarity between 2 vectors(non-zero) of an inner product. Talking about document similarity, it is usually used to find the similarity between 2 documents. These documents are represented as vectors of word frequencies.

Levenshtein distance

This technique measures the differentiation between 2 strings. This is the least number of deletions, substitution, or insertions needed to change 1 string into another.

Jaccard index

The Jaccard index is also known as Jaccard similarity coefficient. IT measures the similar traits between 2 sets. In other words, it is the ratio of size of intersection to the size of union.

Euclidean distance

This technique is a measure of the distance in 2 points in a Euclidean space. You can calculate it as a sq. root of the sum of sq. of the differences of corresponding coordinates of 2 points.

Hamming distance

This technique measures the difference between 2 strings of the same length. In other words, it is the minimum number of single substitutions needed to change a string into another of the same length

Word embeddings

These are administered representations of words. They highlight words as vectors, where every vector dimension has a different feature of the word’s meaning. Word embeddings are usually fundamental in various NLP tasks like text classification, machine translation, and information retrieval.

Text Clustering

K-means

This is a famous unsupervised learning algorithm. K-means is a very straightforward algorithm that separates a single dataset into “k” clusters. Here, k is the parameter specified by the user.

Hierarchical Clustering

This technique creates multiple cluster hierarchies and each of them is a subset of the cluster over it.

DBSCAN

DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise. This is a density-based clustering technique that combines points which are close to each other. It is different from hierarchical clustering and  k-means clustering in the sense that no. of clusters does not have to be set up front.

Latent Dirichlet Allocation

This technique is a generative probabilistic model which is used for topic modelling and text clustering. It is totally based on the idea that every document is a mix of a small number of topics and every topic is a probability distribution above words.

Revolutionary Applications of Text Similarity

1. Document classification: It can be used to classify documents. For instance, you can use text similarity in a document classification system to find out whether the given document makes sense with the given topic
2. Plagiarism detection: Text similarity is used to detect instances of plagiarism by simply comparing the similarity of text to other known documents or texts.
3. Information retrieval: It can be used to find similar documents in a database. For instance, if you search of “book” in the given dataset, you might get documents containing similar words like “journal” or “magazine”
4. Sentiment analysis: It is used to find the sentiment of a textual content by comparing it to pre-classified texts with already known sentiments.
5. Recommendation systems: In streaming services, content platforms, and e-commerce, modern text similarity empower recommendation systems. It ensures users to receive personalized product and content recommendations.
6. Summarization: Text similarity is used to summarise any textual document by recognizing the most vital phrases in the document.
7. Language translation: You can use text similarity to improve the perfection of machine translation systems just by comparing the similar traits of the translated text to the original one.

Revolutionary Applications of Text Clustering

1. Information retrieval: Clustering is used to club similar documents and makes it simpler to find information.
2. Sentiment analysis and opinion mining: You can use text clustering club textual documents that express similar feelings or opinions.
3. Text summarization:  It is used to search for the most important documents in the given dataset, which can be further used to summarise the content in the data set.
4. Classifying text: You can use text clustering as an attractive move to put textual documents in categories that have been set up.
5. Language model improvement: You can use clustering to club textual documents with similar writing style or topics, which can later be used for language model improvement.
6. Topic modelling: Text clustering is used to search for hidden topics in textual documents that can later find how the data is arranged.
7. Marketing: Clustering is a valuable tool for organizing customer feedback, reviews, and survey responses, allowing businesses to gain insights into customer preferences, opinions, and feedback.
8. Social Media Analysis: Clustering is a powerful technique for categorizing social media posts, comments, and tweets, providing a deeper understanding of the overall sentiment and viewpoints regarding specific topics.
9. Fraud Detection: Text clustering plays a crucial role in financial and cybersecurity sectors by grouping related textual data associated with potential threats, aiding in the identification of fraudulent activities and patterns.
10. Healthcare and Life Sciences: Modern text clustering facilitates the organization of vast medical literature, empowering researchers to efficiently navigate and extract pertinent information from extensive text databases.