Sentiment analysis is about detecting emotions, opinions of people about certain topics by analyzing their texts from tweets, fb comments or status , youtube comments so on and so forth. Retail industries and companies in general use sentiment analysis to get an overview of their clients’ opinions on their products which enables them to make improvements and certain modifications to their products so that it meets their clients’ standards. There are a lot of social media sites like Google Plus, Facebook, and Twitter that allow expressing opinions, views, and emotions about certain topics and events.
In this project, we’re going to build a spam filter for SMS messages using the multinomial Naive Bayes algorithm. Our goal is to write a program that classifies new messages with an accuracy greater than 80% — so we expect that more than 80% of the new messages will be classified correctly as spam or ham (non-spam).
To train the algorithm, we’ll use a dataset of 5,572 SMS messages that are already classified by humans.