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build a sentiment analysis model which will allow us to categorize words based on their sentiments, that is whether they are positive, negative and also the magnitude of it.

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sentiment-analysis-model-using-r's Introduction

What is Sentiment Analysis?

Sentiment analysis is the process of analyzing digital text to determine if the emotional tone of the message is positive, negative, or neutral. Today, companies have large volumes of text data like emails, customer support chat transcripts, social media comments, and reviews. Sentiment analysis tools can scan this text to automatically determine the author’s attitude towards a topic. Companies use the insights from sentiment analysis to improve customer service and increase brand reputation.

Why is sentiment analysis important?

Provide objective insights
Build better products and services
Analyze at scale
Real-time results

What are sentiment analysis use cases?

Improve customer service
Brand monitoring
Market research
Track campaign performance

What are the different types of sentiment analysis?

Fine-grained scoring
Aspect-based
Intent-based
Emotional detection

Reference : Amazon

Sentiment Analysis Model:

We will carry out sentiment analysis with R in this project. The dataset that we will use will be provided by the R package ‘janeaustenR’.

janeaustenr: Jane Austen's Complete Novels:

Full texts for Jane Austen's 6 completed novels, ready for text analysis. These novels are "Sense and Sensibility", "Pride and Prejudice", "Mansfield Park", "Emma", "Northanger Abbey", and "Persuasion". janeaustenr
In order to build our project on sentiment analysis, we will make use of the tidytext package that comprises sentiment lexicons that are present in the dataset of ‘sentiments’.

kaggle

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