This is a project on sentiment analysis using machine learning algorithms. Sentiments are basically an expressed opinion or view and it can be of various types like positive-negative, neutral, happy-sad etc. Sentiment analysis is a tool used to analyze texts for polarity i.e. positive to negative. By training the machines, they automatically learn to detect sentiment without human input.These models can detect beyond mere definitions like sarcasm, context or misapplied words.
In this repository you will find:
- Code
- Dataset - https://www.kaggle.com/datasets/sameersmahajan/reviews-of-amazon-baby-products
- Results (Screenshots)
- Reports (also contains Weekly Reports)
- Presentations
References for the project - [1] Sentiment Analysis & Machine Learning. MonkeyLearn Blog. (2020, April 20). Retrieved March 20, 2022, from https://monkeylearn.com/blog/sentiment-analysis-machine-learning/#:~:text=Sentiment%20analysis%20is%20a%20machine,detect%20sentiment%20without%20human%20input
[2] Performing sentiment analysis with naive Bayes classifier! Analytics Vidhya. (2021, July 13). Retrieved March 20, 2022, from https://www.analyticsvidhya.com/blog/2021/07/performing-sentiment-analysis-with-naive-bayes-classifier/
[3] Foy, P. (2021, July 26). Naive Bayes for sentiment analysis & Natural Language Processing (NLP). MLQ.ai. Retrforved March 20, 2022, from https://www.mlq.ai/sentiment-analysis-with-naive-bayes/
[4] Kuzminykh, N. (2020, October 23). Sentiment Analysis in python with textblob. Stack Abuse. Retrieved March 20, 2022, from https://stackabuse.com/sentiment-analysis-in-python-with-textblob/