Data-science-project

Methodology -

• We take our input keyword, all the tweets are being automatically shown.
• For example, if we enter a keyword as "Trump", so, all the tweets about trumps will be shown.
• We can keep a limit on how many tweets we want. If we want 10 tweets, then only 10 tweets will come up.
• So, next, we find out the polarity & subjectivity of these tweets.
• polarity means = how +ve or -ve the text is [-1,1]
• subjectivity means how much of an opinion it is vs factual
• Then we store all these in a CSV file (comma seperated file)

• So, let me tell you how it works,
• To access tweets we use the Twitter API. So, API means - application programming interface - it is a gateway that lets u access some server's internal functionality, in our case twitter
• So, when we take an input text like a tweet, we split it up in several words or sentences. this process is called tokenization.
• we count the number of times, each word shows up, once the text is tokenized, then we look up the sentiment value for each word from a sentiment lexicon that has it all already, pre-recorded to classify the total sentiment value of our tweet

Dependencies used -

• tweepy - whch access twitter api
• textblob - perform actual sentiment analysis
• pandas - Pandas is mainly used for data analysis. Pandas allows importing data from various file formats such as comma-separated values, SQL, Microsoft Excel. It allows various data manipulation operations such as merging, reshaping, selecting, as well as data cleaning, and data wrangling features.
• time - control time

Code Editor

• Anaconda- A web based environment to execute python files