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Text Analitics with Jaseci

You will gain an understanding of the several text analysis methods offered by Jaseci in this course. After completing this codelab, you will be able to map the the content of a movie script into a graph and analyze and comprehend it. Let's examine the actors' dialogue flow and what they say throughout the course of the film. Exited? SO lets dive in.

📄️ Find Semantically Similar Sentences

Semantic similarity of two sentences is a measure of how closely related their meanings are. It involves comparing the underlying semantic representations of the sentences to determine the degree of overlap or similarity between them. This is typically done using techniques from natural language processing (NLP), such as word embeddings or semantic networks. The resulting similarity score can be used in various applications, such as text classification, question answering, or information retrieval, to identify relevant and related content. A high semantic similarity score suggests that the two sentences convey similar ideas, while a low score indicates that they are dissimilar.

📄️ Find Similar clusters in a set of documents

Text clustering, also known as text grouping or document clustering, is a technique used in natural language processing (NLP) and machine learning to categorize large sets of unstructured textual data into meaningful groups or clusters. The goal of text clustering is to identify patterns and relationships within the text that can be used to group similar documents together based on their content, topics, or other features. This can help researchers, businesses, and organizations to better understand the underlying structure of their textual data and to identify important insights or trends that may be hidden within it. Text clustering is often used in applications such as document organization, information retrieval, and text summarization.

📄️ Analyze sentiments in Dialogues

Sentiment analysis is a process of analyzing text data to determine the emotional tone or attitude expressed in it. It involves using natural language processing and machine learning techniques to identify and extract subjective information from text, such as opinions, attitudes, and emotions. Sentiment analysis is commonly used in social media monitoring, customer feedback analysis, brand reputation management, and market research. It can help organizations gain insights into customer sentiment, identify emerging trends, and make data-driven decisions. Sentiment analysis can be performed at different levels, including document-level, sentence-level, and aspect-level analysis. The output of sentiment analysis is typically a numerical score or a categorical label that indicates the polarity of the text, such as positive, negative, or neutral.