DataHour: Using Word & Document Embeddings for Sentiment Analysis
DataHour: Using Word & Document Embeddings for Sentiment Analysis
23 Dec 202213:12pm - 23 Dec 202215:12pm
DataHour: Using Word & Document Embeddings for Sentiment Analysis
About the Event
This is the second session of the 3-part series on building and deploying a Sentiment Analysis project from scratch. If you have missed the 1st session, you can view the recording here: https://www.youtube.com/watch?v=4XqAK5HEobE
In this session we'll cover the Practical implementation of Word Embedding Models (Word2Vec, GloVe), Document Embedding models (Doc2Vec), and finally language models like ELMo for sentiment analysis.
The 3rd part in the series will cover Deployment of the models built in Parts 1 & 2 on local machine & cloud.
Detailed:
Word embeddings can be used in sentiment analysis to represent the words in a piece of text in a continuous vector space, capturing the relationships between words and their meanings. In sentiment analysis, the goal is to classify text as having positive, negative, or neutral sentiment. Word embeddings can be used to represent the words in the text being analyzed and these embeddings can then be used as input to a machine learning model that is trained to classify the sentiment of the text.
By capturing the context-dependent meanings of words, word embeddings can help improve the performance of the sentiment analysis model. For example, the word "good" may have a positive connotation when used to describe a product, but a negative connotation when used to describe a person. Word embeddings can capture these nuances and help the sentiment analysis model make more accurate predictions.
In addition to being used as input to a machine learning model, word embeddings can also be used to measure the similarity between words or pieces of text. This can be useful in sentiment analysis, as it can help identify words or phrases that are associated with positive or negative sentiment. For example, if a piece of text contains a high number of words that are similar to words that are commonly associated with positive sentiment, it is likely that the text has a positive sentiment overall.
Prerequisites: Basic understanding of Python programming and strong interest in NLP.
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Who is this DataHour for?
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