Twitter Sentimental Analysis

The Positive words in Tweets

Introduction

Sentiment Analysis is a technique used in text mining. It may, therefore, be described as a text mining technique for analyzing the underlying sentiment of a text message, i.e., a tweet. Twitter sentiment or opinion expressed through it may be positive, negative or neutral.

The Negative words in Tweets

As a part of Natural Language Processing, algorithms like Naive Bayes is used in predicting the polarity of the sentence.

In this hands-on project, we will train a Naive Bayes classifier to predict sentiment from thousands of Twitter tweets. This project could be practically used by any company with social media presence to automatically predict customer’s sentiment (i.e.: whether their customers are happy or not). The process could be done automatically without having humans manually review thousands of tweets and customer reviews.

• Import libraries and datasets
• Perform Exploratory Data Analysis
• Perform data cleaning - removing punctuation
• Perform data cleaning - remove stop words
• Perform Count Vectorization (Tokenization) and TFIDF Vectorization
• Build the suitable algorithms
• Assess trained model performance

Accuracy with Naive-bayes: 94.2 %

Find my code at: MPrathyusha01/Twitter-Sentimental-Analysis

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