Social Media Sentiment Analysis using twitter dataset

Sentiment analysis is basically the computational determination of whether the piece of content is positive or negative. This analysis is also known as Opinion Mining; it earns a great use in today’s world. This application can be helpful in deciding the sentiments in the tweets of the people. As Twitter is a huge platform for opinions, and it affects a large number of people, the application can be helpful in reducing the hatred on the Internet.

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Why sentiment analysis?

Sentiment Analysis is very useful in major fields such as Business in marketing fields, in politics to predict the view of debates, in public actions to monitor the public phenomenon. This application can be developed using various algorithms, and the program is written in python language.

Project Implementation

The project is comprised of three major steps. The first step is to authorize the Twitter API clients, which will provide us the tweets. Then you have to make a GET request to the API client; it will help to fetch the tweets for a particular query. Then, at last, the tweets can be qualified as positive, negative, or neutral by using the algorithms.

First, you will need to create a twitter client class which will contain all the ways to interact with the API. For this (init) function can be used to authenticate the API client. Using get_tweets function, you can fetch the tweets to analyze it. The TextBlob library is a high-level function library built to process the text provided by the API.

What are the things to be considered?

The tweet is to be tokenized, which means separating the word from the tweets. The irrelevant words are to be removed to process it well. The selection of token is very important as they will be passed to the sentiment classifier. The sentiment classifier will classify the tweets as positive, negative, or neutral.

Working of the Sentiment Classifier

The TextBlob uses the movie reviews as a dataset; the reviews act as a parameter for decision. The application will be trained by the TextBlob as the reviews can act as the mark of positivity or negativity.

Conclusion

The results will signify the output in -1 to 1 as -1 will give the output as negative, one will give the output as positive, and 0 will signify the output as neutral. The application is ready to analyze the data and make the statistics of tweets, which will be helpful.

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Social Media Sentiment Analysis using twitter dataset