Sentiment Analysis is a branch of Natural Language Processing (NLP) that allows us to determine algorithmically whether a statement or document is “positive” or “negative”.
Sentimental Analysis can be used in many Industries to generate Insights from the unstructured text Data. This repository covers usage of sentimental Analysis in few of such Industries.
This repository contains three notebooks -
Twitter Sentiment analysis for predicting Canada Elcetion results
Sentimental Analysis of Airline tweets to understand the market sentiment
Above two analysis uses various Machine learning algorithms for classifying the postive and negative tweets.
Data Science and NLP for Customer Review Analysis - Hotel reviews: This involves performing analysis of real hotel review data crawled from the Tripadvisor website to automatically identify positive and negative keywords and phrases associated with hotels and to better understand characteristics of data analysis tools, extracting explanatory review summaries, and human reviewing behavior.