Sentiment Analysis of Tweets
It is a web app project based on Natural Language Processsing (NLP). The idea is to fetch tweets based on a keyword, clean the tweets and classify them in different Setiment categories like as positive, negative, strongly positive, neutral etc. With this we can know how people thinks about a particular topic on twitter. This is the sentiment analysis of tweets where we can analyse how people are reacting.
Sentiment Analysis come in handy in case of reviews i.e how much audience liked a movie, how much positive reviews are there for a product and many more.
Natural Language Processing
Natural Language Processing, usually shortened as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. The ultimate objective of NLP is to read, understand, and make sense of the human languages in a manner that is valuable. Most NLP techniques rely on machine learning to derive meaning from human languages.
1. Here I have used Tweepy module that is used to fetch tweets from twitter using some authentic keys.
2. Fetched tweets are then cleaned by removing special characters and links to make text relevent for processing.
| Before Cleaning | After Cleaning |
|---|---|
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3. Polarity of each tweet is calculated
Mathematically Polarity is defined as float which lies in the range of [-1,1] where 1 means positive statement and -1 means a negative statement.
polarity = 0 ----> Neutral
polarity = 0 to 0.30 ----> Weak Positive
polarity = 0.30 to 0.60 ----> Positive
polarity = 0.60 to 1.00 ----> Strong Positive
polarity = -0.30 to 0 ----> Weak Negative
polarity = -0.60 to -0.30 ----> Negative
polarity = -1.00 to -0.60 ----> Strong Negative
3. Percentage for each sentiment or polarity is calculated separately like for positive, negative, neutral etc.
4. The derived information is then plotted using a Pie chart
